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Blockchain@UBC has published a number of research papers, through various academic partners and collobarative efforts. Explore more in detail through the list on this page.

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IEEE Potentials, November/December 2022 - Blockchain Technology

IEEE Talks Blockchain

In our latest IEEE Talks Blockchain Q&A session, we spoke with Dr. Ramesh Ramadoss, co-chair of the IEEE Blockchain Initiative. Dr. Ramadoss discusses the evolution of the blockchain ecosystem, including emerging applications, standardization efforts, and opportunities for future growth and development.

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IEEE Blockchain Transactive Energy (BCTE) Position Paper

This Position Paper describes the basic framework and principles for using blockchain technology in power and energy domains with the emerging participatory grid. A key goal is the development of the most promising global Transactive Energy use cases which can be advanced toward broader commercialization using blockchain technology.

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Reinforcing the Links of the Blockchain IEEE Future Directions - November 2017

The purpose of this white paper is to explore the various ways by which the IEEE can lead and support an initiative on Blockchain while providing educational materials that will foster the next generation of blockchain engineers. This white paper summarizes and expands upon the IEEE Blockchain Incubator Workshop held by IEEE Future Directions at the end of October in 2017.

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IEEE Blockchain Articles

Blockchain for Business Value: A Contract and Work Flow Management to Reduce Disputes Pilot Project IEEE Engineering Management Review - December 2018

By Liang Xi Downey; Frédéric Bauchot; Jos Röling

Blockchain technology has seen signiﬁcant growth, hype, and potential new developments over the past few years. In this article additional insights into how blockchain can add value to a business process relationship is detailed. Speciﬁcally an engineering contract workﬂow use application pilot including various high level system architectural aspects are presented. This application shows the integration of blockchain technology with existing legacy systems. Some management and technology issues are also overviewed for the reader.

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Enhanced Distributed Ledger Technology NIST Computer Security Resource Center - September 2019

The blockchain data structure and proof-of-work protocol were designed to solve the problem of double spending in cryptocurrencies. Although blockchain has found many applications outside of cryptocurrency, many of its features are not well suited to common data management applications. The added trust of distributed ledgers is a valuable feature, providing greatly simplified auditability and verification of actions among multiple parties in applications such as supply chain and others, but there are tradeoffs.

Blockchain's hash-based integrity verification provides trust, at the cost of an inability to delete or update records, leading to design complications that would not arise with conventional database management systems. Similarly, the sequencing guarantees of blockchain consensus protocols are needed for cryptocurrency in the absence of a universal timestamp. Moreover, actions within the distributed ledger must be connected with other actions in the real world, through accurate timestamps. We are developing a new architecture that provides the trust features of blockchains, with characteristics that allow for simpler designs and greater practicality in conventional data management problems. We believe this alternative can lead to new approaches to incorporating trust into distributed systems applications.

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Review article, a review on blockchain technology and blockchain projects fostering open science.

1 Department of Business and Industrial Engineering, Offenburg University of Applied Sciences, Offenburg, Germany
2 Department of Mathematics, FIZ Karlsruhe - Leibnitz-Institute for Information Infrastructure, Berlin, Germany
3 School of Electrical, Information and Media Engineering, University of Wuppertal, Wuppertal, Germany

Many sectors, like finance, medicine, manufacturing, and education, use blockchain applications to profit from the unique bundle of characteristics of this technology. Blockchain technology (BT) promises benefits in trustability, collaboration, organization, identification, credibility, and transparency. In this paper, we conduct an analysis in which we show how open science can benefit from this technology and its properties. For this, we determined the requirements of an open science ecosystem and compared them with the characteristics of BT to prove that the technology suits as an infrastructure. We also review literature and promising blockchain-based projects for open science to describe the current research situation. To this end, we examine the projects in particular for their relevance and contribution to open science and categorize them afterwards according to their primary purpose. Several of them already provide functionalities that can have a positive impact on current research workflows. So, BT offers promising possibilities for its use in science, but why is it then not used on a large-scale in that area? To answer this question, we point out various shortcomings, challenges, unanswered questions, and research potentials that we found in the literature and identified during our analysis. These topics shall serve as starting points for future research to foster the BT for open science and beyond, especially in the long-term.

1. Introduction

The blockchain technology (BT) offers great potential to foster various sectors ( Casino et al., 2018 ) with its unique combination of characteristics, for example, decentralization, immutability, and transparency. We see promising possibilities in the use of this technology for science and academia. In this paper, we want to show why the BT suits especially to open science. So far, the most prominent attention the technology received was through news from industry and media ( Morini, 2016 ; Notheisen et al., 2017 ; Carson et al., 2018 ; Volpicelli, 2018 ) about the development of cryptocurrencies. Examples are Bitcoin, Litecoin, Dash, and Monero, which all are having remarkable market capitalizations 1 . BT, however, is not limited to cryptocurrencies. There are already existing blockchain-based applications in industry and the public sector like crowdfunding ( Conley, 2017 ; Li and Mann, 2018 ; Arnold et al., 2019 ), tracking of goods in supply chains ( Abeyratne and Monfared, 2016 ; Tian, 2016 ; Hepp et al., 2018 ), authentication ( Cruz et al., 2018 ; Ihle and Sanchez, 2018 ), and voting services ( Swan, 2015a ; Osgood, 2016 ); many more are under development ( Brandon, 2016 ; Davidson et al., 2016 ; Fanning and Centers, 2016 ; Nguyen, 2016 ; Scott, 2016 ). The Fraunhofer Institute for Scientific and Technical Trend Analysis (INT) in Germany published a study ( Schütte et al., 2018 ) showing that currently BT can be most frequently found in applications used in the financial sector.

The typical use case in that area for BT is the exchange of value units without the need of intermediaries ( Nakamoto, 2008 ; Ben-Sasson et al., 2014 ). Examples for that are the already mentioned cryptocurrencies and other applications that, for instance, allowing individuals to offer and sell their digital assets like art or data from sensors on a marketplace ( Draskovic and Saleh, 2017 ), or enabling property owners to transfer their land without a notary ( Kombe et al., 2017 ). The pioneering role of the financial sector seems obvious because cryptocurrencies were the first usable blockchain applications. Nevertheless, the potential of this technology has attracted the attention of other areas in recent years, leading to a vast number of new projects 2 . BT is still in an early development phase without widely adopted standardization and frameworks yet.

There are already some scientific sources (but far more gray literature) on how the BT can be used to mitigate existing problems in science like the reproducibility of results from published articles and experiments. Due to immutability, append-only function, and a viewable record of all transactions, BT can provide transparency for all users over every step done in a system. As a result of that, an environment gets created that does not need a trusted authority because malicious behavior is technically difficult. The decentralization enables researchers to build their own open ecosystem for research data, metadata, and communication that follows the philosophy of open science. For us, open science is characterized above all by the fact that everyone can openly participate, collaborate, and contribute to science. The results of these activities, such as research data, processes, studies, and methods, are freely available so that they can be reused and reproduced. In section 3, we go into open science and its definitions in more detail.

Besides reproducibility of experiments ( Prinz et al., 2011 ; Collins and Tabak, 2014 ; Gilbert et al., 2016 ; Furlanello et al., 2017 ), BT can also get used to address several other scientific problems ( Gipp et al., 2015 , 2017 ; Anonymous, 2016 ; Dhillon, 2016 ; Golem, 2016 ; Wolf et al., 2016 ; Breitinger and Gipp, 2017 ; van Rossum, 2017 ; Androulaki et al., 2018 ; Bartling, 2018 ; Janowicz et al., 2018 ) like trust problems in the form of malicious behavior in peer-review processes ( Stahel and Moore, 2014 ; Degen, 2016 ; Dansinger, 2017 ), lacking quality and redundancy of study designs ( Macleod et al., 2014 ; Belluz and Hoffman, 2015 ), and the restriction of free access to scientific publications ( Myllylahti, 2014 ; Teplitskiy et al., 2017 ; Schiltz, 2018 ). BT also has the ability to increase the trustability of studies and collaborations among researchers in complex science projects by the use of its characteristics.

BT stands out from other systems in its exceptional technical architecture, which allows the technology to get adapted for a variety of use cases. For example, developers have the possibility to design blockchains for open or private access combined with individual governance models depending on its purpose. In addition to the technical perspective, cryptocurrencies, for example, provide additional, unique opportunities to create business models and incentives for users or entire communities. However, besides BT, there are also other technologies that are applicable to open science. One example is the peer-to-peer data synchronization protocol Dat ( Ogden et al., 2018 ) that also supports immutable and decentralized storage and can be used as an infrastructure for scholarly communication ( Hartgerink, 2019 ). The protocol got inspired by several existing systems, one of them being BitTorrent ( Pouwelse et al., 2005 ). Further non-blockchain-based approaches supporting open science include the research and collaboration platforms Open Science Framework (OSF) ( OSF, 2019 ) and OPERAS ( Mounier et al., 2018 ), the open access repository Zenodo ( Zenodo, 2019 ), the research data infrastructure offered by the European Science Cloud (EOSC) ( EOSC, 2019 ), and the publishing platform F1000Research ( F1000 , 2019 ).

We want to point out at an early stage of this paper that BT is just a technology and certainly not the silver bullet that will overcome all problems we are facing in science today. Some of the issues cannot get solved by technology alone, instead require the involved persons to rethink habits, behaviors, and processes. In some cases, it might even lead to researchers having to renounce privileges. There is also criticism of the use of BT for science. Hartgerink (2018) argues that blockchains can even amplify inequalities by increasing artificial scarcity and relying on free market principles. Another point of criticism affects the consensus principle as the fundamental definition of truth in a blockchain. Firstly, there is always a chance of hijacking a blockchain with a so-called 51%-attack. Secondly, and more relevant from a philosophical point of view, Hartgerink asks whether we need a consensus for scientific theories or ideas at all.

Overall, our work contributes to understanding the BT and the possibilities it offers to design, implement, and improve open science projects and applications across all different scientific fields. We think it is a suitable technology to support the transformation of open science. The motivation for this work lies in the circumstance that there is currently no systematic review of the general suitability of BT for open science, the state of the art or related vital challenges and research potentials. We are addressing these topics in this paper.

The BT is, besides the financial area, also emerging in many other sectors and gets continuously more popular. It is difficult to overview the market of existing and planned projects since there is no holistic public database or repository for it. Further, the range of visions, concepts, and prototypes is constantly increasing, which means that this review can only provide a snapshot and does not claim to be complete or exhaustive.

We conducted a systematic review of the research topic by first searching for relevant literature. It has turned out that this topic is quite novel, and there are just a few publications about how BT can be used to foster open science or science in general. In a literature review about the usage of BT in different domains ( Casino et al., 2018 ), the application field of science did not even get mentioned as an application domain. Besides literature, we also focused our analysis on various blockchain projects that can foster open science in different ways. We want to provide a transparent and reproducible review, thus in the following, we describe our research questions and methodology.

1. What are the current requirements for a technical open science infrastructure, and how do they compare with BT features?

2. What is the current status and perspectives for the use of BT in science and academia?

3. What are the biggest challenges and obstacles that are preventing successful implementation and adoption of BT as supporting infrastructure for open science?

(1) We approached this question by comparing the characteristics of BT with the goals and needs of open science. We examined whether it is able to deliver a reasonable and adequate fundament for an open science ecosystem. At first, we studied existing literature to describe what open science is (section 3.1), what it aims to be, and what the requirements for such an infrastructure (section 3.2) are. Then, we examined the BT to understand how it works and what characteristics it has (section 4.1). Finally, we created a matrix that shows all related infrastructure requirements and compares them to the characteristics of the BT to determine how they match and whether they can be fulfilled (section 4.2).

(2) To answer the second research question, we discussed relevant literature, gray literature, and projects that we found, collected, and screened from different search engines and reference lists until April 2019. Primarily, we used Google Scholar 3 , PLOS 4 , CiteSeerX 5 , Microsoft Academic Search 6 , and GitHub as file hoster of software development projects. Secondarily, we examined research publications, whitepapers, and blogs. We found the most relevant literature and projects by using the search terms “blockchain” with “science,” “publishing,” “peer review,” and “reproducibility.” The relevance of literature was made sure by reading their abstracts and, partially, the whole work if the abstract was not clear enough to rate the specific content. If a paper had no meaningful content for our research, we excluded it from our review. From there on, we screened the reference lists of the remaining literature to find further suitable sources, known as snowballing. After that, we made a full-text review of the content of all papers to get an overview of the current research state that showed the potential and increasing interest in the BT for open science (section 5.2).

Besides the literature, we also collected exciting and promising blockchain-based projects consisting of concepts, prototypes, and already deployed applications. We found in numbers many more projects than relevant scientific publications. The majority of the projects got identified in the reviewed literature and the rest through search engines. These projects are either designed specifically for open science, or some of their functionalities are usable in that area. We also found some very early concepts and ideas that only exist in forums or social media networks. However, their potential is not ratable yet due to low progress and information scarcity, so we did not include them into detailed analysis. Altogether, we collected and analyzed 83 projects but removed 23 of them early due to cancelation, irrelevancy, or inactivity (no actions or news for more than 1 year), leaving 60 projects left. We summarized and mapped these into different categories according to their use and created an overview of our approach (section 5.1). The so built structure and the review of projects help to gain a better understanding of the current situation of research in this area (section 5.3). Finally, we made a summary and discussed our findings (section 5.4). For a complete overview, we created a database (see Supplementary Material ) containing a short description, project state, and other characteristics for each project.

(3) As a basis to process the third research question, we used the knowledge gained from answering the first and second research question, and the analysis of literature and projects. First, we conducted a brainstorming, discussed all mentioned topics, and rated them each individually. Then we created a ranking of the topics by collecting and evaluating the ratings of all people who were involved in the brainstorming. Finally, we took the issues of rank one to five and described them in terms of current challenges, research potentials, and open questions that should be addressed to foster the BT for open science (sections 6.1–6.5).

3. Open Science

In this section, we briefly describe the philosophy behind open science and existing problems in science it can mitigate (section 3.1). Furthermore, we did an analysis to point out what requirements have to be met to establish a technical ecosystem that follows and lives the principles of open science (section 3.2). Finally, we created an overview of the requirements we determined in this section.

3.1. Overview

There are several definitions of what open science is, but there is not a universal definition that is generally valid. We think the definition of FOSTER 7 is a good representation of the term: “Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods.” There are other descriptions such as the “open” definition 8 and one from the OECD 9 . An illustrated story about the development of open science can be viewed here ( Green, 2017 ). Overall, open science is an umbrella term for a multitude of assumptions about how the future of knowledge creation and dissemination (also education) will work ( Fecher and Friesike, 2014 ). There are different types of implementations, such as sharing of computing and storage resources in an open science grid (OSG) ( Pordes et al., 2007 ; Altunay et al., 2011 ) or open access repositories for research literature as SocArXiv 10 , CiteSeerX, and arXiv 11 . We want to briefly discuss open science in its chances and challenges to provide a common point of definition from that we will link the possibilities of BT to the fundamental concept of open science. Fecher and Friesike (2014) structured open science in five schools of thought and Tennant et al. (2019) expanded them by a sixth (see Table 1 ). It summarizes the identified schools with their central assumptions, their goals, and keywords.

Table 1 . Six open science schools of thought. The sources ( Fecher and Friesike, 2014 ; Tennant et al., 2019 ) got combined.

As we have learned only late about the sixth school (community school), which is also quite new, we refer in the further work to the original five schools, which are the basis of our requirements analysis. For the sake of completeness, we included the sixth school in Table 1 . After analyzing the community school, we can say that the result of this review would not have changed if it had been included, on the contrary, the principles of this school harmonize well with the characteristics of the BT. However, it should get considered in future research work.

Today's communication technologies have opened up the way to practice open science; in detail, the methods for producing, storing, sharing, and accessing information have been progressing, and new research opportunities have developed ( Nentwich, 2003 ). Opening research processes provides, among other things, the chance to get valuable feedback from other researchers for work in progress, for example, through a platform like the Open Science Framework (OSF) 12 ( Bartling and Friesike, 2014 ). It can be called scientific-self correction if the scientific community and also non-experts are able to access research data while it is still in process and to provide feedback in the form of possible mistakes and potential improvements of the underlying work. Such an approach can also help to find solutions to specific problems more efficiently ( Bartling and Friesike, 2014 ).

Adjustments in science are needed because many studies in different scientific fields, for example, medicine, psychology, and computer science are irreproducible ( Schooler, 2014 ; ASCB, 2015 ; Baker and Penny, 2016 ; Smith, 2017 ); sometimes even the original researchers are not able to reproduce the results of their earlier experiments ( Pashler and Wagenmakers, 2012 ). That situation is known as reproducibility crisis, and the open principles are a promising approach to mitigate such a problem, as it can make research more transparent and understandable. We would also like to mention that there are critical voices that do not see a reproducibility crisis in science and calling it a narrative. For example, Fanelli (2018) concludes a literature review on that topic with the statement that it is empirically unsupported to say science would be undergoing a reproducibility crisis. Rather, it would be counterproductive fostering cynicism and indifference among young researchers instead of inspiring them to do more and better research.

Researchers usually aggregate and compress their collected research data for their final publication to meet the requirements of journals and especially conferences that request to stay within a specific limit of pages. In computer science, the cap for full papers on conferences is mostly ten pages ( Gray, 2009 ). So other researchers often have no access to the unedited raw data that can be very useful for the understanding and reproduction of the results of a paper. The aggregated data often lacks the needed degree of detail to reproduce the process of creation ( Murray-Rust, 2008 ). The transparency of open science shall serve as an example of how it can foster and improve general scientific procedures. However, researchers need a secure and trustable environment for that purpose.

In addition to the raw data, researchers create further content such as ideas and study designs in early research phases that usually do not get published. If the experiments and analysis give negative results, the same picture appears since the focus is on publishability ( Nosek et al., 2012 ) and publication bias for positive results exist ( Matosin et al., 2014 ; Van Assen et al., 2014 ; Mlinarić et al., 2017 ). So, the current system in science leads to the waste of much potentially valuable data ( Van Assen et al., 2014 ; Mlinarić et al., 2017 ). An open research culture during all phases along the research cycle with published supporting data can enhance the quality of work. Supplementary, from an economic perspective, researchers may check ongoing projects to prevent the waste of time and resources for topics that are already getting processed by others.

Open science still has to overcome significant obstacles in different dimensions to get widely applied. Most of the points mentioned here require such drastic changes in research processes and habits and behaviors of researchers that their realization in the foreseeable future is doubtful. For example, the traditional workflows of researchers need to be changed; they usually do not contain steps to publish research data or publicly discuss different topics about it before the final publication. Research is most of the time taking place in a closed institutional framework without the integration of individuals from the outside, so these barriers need to put down to build an open research environment. Around the whole open science discussion, a legislative framework has to be developed, but not only on the national level; it has to be international to set the global rules for the disclosure of incoming and outgoing data and also to protect the rights of all people involved. It is also a discussion of how the crediting of contributions is working fairly when researchers are creating micro-contributions (data sets, hypothesis, ideas, and reviews) ( Tennant, 2018 ) in addition to traditional publications.

Altogether, in this section, we described on the one side different challenges and problems of science and the other side how open science can mitigate them and what benefits it can deliver if a suitable technical infrastructure is found. For that purpose, we are analyzing in the next section what specific requirements such an open science infrastructure has to fulfill.

3.2. Requirement Analysis for an Infrastructure

With the underlying five schools of thought by Fecher and Friesike (2014) , we systematically analyzed what requirements for an open science infrastructure following the open principles are. Therefore, we first made a detailed requirement list of every school and compressed them to a superordinate and more abstract level. Then we identified cross-school elements of such an ecosystem by checking if certain schools sharing the same needs. Finally, we have assigned all other requirements to the specific schools. Out of this analysis, we created a overview of requirements (see Figure 1 ). In the following paragraphs, we briefly describe all single points.

Figure 1 . Overview of general and specific requirements for an open science infrastructure/ecosystem.

One essential requirement of an open science infrastructure is to provide a collaborative environment , which means that researchers and also non-experts are able to work together, author collaboratively, and share information, materials, reagents on different projects ( Hunter and Leahey, 2008 ; Tacke, 2010 ). The performance in a (research) team compared to single researchers is far more effective and efficient on different levels, for example, better quality, higher productivity, and fewer errors by additional review bodies. The requirements Open Data and Open Access are supporting the collaborative environment while they address different scientific problems. Open Access portrays free access to knowledge, for example, scientific publications ( Cribb and Sari, 2010 ; Rufai et al., 2011 ; Sitek and Bertelmann, 2014 ). Quite often, research publications are behind a paywall with continuously increasing costs ( Carroll, 2011 ) that can hinder researchers and the general public from reading and citing them; ironically, research is often funded by tax money. Among other researchers, Cribb and Sari describe the access to knowledge as a necessity for human development ( Cribb and Sari, 2010 ; Phelps et al., 2012 ). One aspect of Open Data addresses the reuse of published scientific data ( Pampel and Dallmeier-Tiessen, 2014 ). Often, an academic third party like a publisher holds the rights, so the scientific community is not allowed to reuse this data without permission ( Murray-Rust, 2008 ; Molloy, 2011 ). Considering the philosophy behind open science, research results should be reusable preventing the waste of resources for collecting already existing data again and allowing for synergies between researchers and their works ( Murray-Rust, 2008 ).

Everyone should be able to express their opinion freely without being censored in any way as long as the law is respected. The same applies to science ( Salyers, 2002 ) and related networks; censorship should not be possible in any way by any participant. We think that there should not be an entity that controls a scientific infrastructure and data on it; rather, collaborative management is preferable in an open science environment. However, each platform needs a governance model that provides the framework for the user community. In this regard, still many questions have to be answered in future work, for example, who initiates, develops, and maintains the platform, who creates the rules and decides about contributions and which parties are trustworthy?

Another essential requirement is to provide an identification and reputation system that can identify researchers and other participants of the ecosystem and link them to their contributions. So, it should be possible to credit the valuable work and invested effort of all contributors appropriately and to calculate scientific metrics, for example, impact factor or h-index to build a reputation ( Woolston, 2015 ). The last general requirement we identified is that every element in a technical infrastructure should be extensible to make sure the whole ecosystem is sustainable ( De Roure et al., 2008 ). Extensibility is vital, especially in today's digital age in which computer technology develops so fast and delivers more efficient new tools regularly. Overall, it allows the community of the ecosystem to upgrade and improve the single components steadily, so no costly and time-consuming substitutes are necessary for the long-term.

The five schools of thought have their own more specific requirements for an open science ecosystem. The democratic school demands incentives for collaboration and sharing of data that are crucial for such an environment ( Arazy et al., 2006 ; De Roure et al., 2008 ; Haeussler, 2011 ). Participants should get an extrinsic motivation, for example, a form of counter-value ( Haeussler, 2011 ) for sharing their data and contributions in an open infrastructure ( De Roure et al., 2008 ). Incentives can also work in harmony with a reputation system. The democratic school also highlights that all users in an open science environment should be treated equally , for example, in the perspective of access to knowledge ( Rufai et al., 2011 ; Fecher and Friesike, 2014 ). So, no participant has more rights than another except in terms of administration and governance of such an infrastructure, which represents a special matter. Decisions about the future development of an ecosystem and how valuable contributions are should be made democratically by independent experts, so in our case, people who have experience in research and the scientific system.

In the view of the pragmatic school , the integration of an open research process into existing established procedures needs to be as simple as possible to convince researchers to change or adjust their workflows . If it is complex, costly, or challenging, it will be a deterrent, so most researchers will not adapt their processes and hence not participate in the network. The complexity also affects the willingness of the researchers to provide and share data and content in general ( Vision, 2010 ). If integration takes too much time, or there are no visible incentives or counter-values, information very likely will not be shared ( Campbell et al., 2002 ; Vision, 2010 ; Boulton et al., 2011 ). The effort needed is a crucial element for a working open infrastructure; simplicity lays the foundation for participation and complements the used incentive systems.

Crowdfunding opportunities in an open science ecosystem are one of the requirements of the public school . It allows that every participant can decide privately to fund individual research projects that are following promising goals; thus, crowdfunding expands funding methods of research. In exchange, these backers can get monetary or non-monetary (for example, usage rights) benefits ( Fecher and Friesike, 2014 ). Furthermore, the public school aims to record the trail of research for every research object like papers, data sets, ideas, used tools, results, and hypothesis so that the involved people get credit for these objects according to their contribution. It is also an important factor to retrace the creation process of, for example, a study or an experiment to replicate its results. A chronological chain of milestones about data creation, and also the availability of raw data can be part of the solution for the current reproducibility crisis. There are two crucial points to fulfill the requirement of a trail of research. First, the researchers need to make proper documentation about their works what they always should do ( Vasilevsky et al., 2013 ). Second, the underlying technical system should record all transactions immutable, so censoring is not possible in any way afterwards.

Another part of the public school is citizen science ( Hand, 2010 ; Gura, 2013 ) that allows regular citizens to participate in certain research projects, even if they have no specific experience in science. A fictional example would be the setting up of temperature sensors in the homes of various participating citizens throughout the world; thus, the global average temperature can be determined. There are several examples of citizen science projects ( Irwin, 2006 ; Hand, 2010 ; Catlin-Groves, 2012 ) - see Rosetta@Home 13 , EchidnaCSI 14 , or eOceans 15 . Opening up research processes to citizens can be beneficial, but it strongly depends on the nature and goals of the particular project ( Irwin, 2006 ; Powell and Colin, 2009 ; Gura, 2013 ). Therefore, an open science infrastructure should provide possibilities to integrate the wide publicity into research.

The infrastructural school contains the requirement of using open source code and tools in projects that include the development of new software ( Nentwich, 2003 ). That procedure enables other researchers to use the same algorithms and processes, which eases the reproduction of results and a general understanding of unknown programs. Schubotz et al. (2018) published a practical guide about using open source tools over the complete research cycle that supports researching by the open principles. One more requirement of the infrastructural school is the ability to share resources like digital storage space or computing power; one example is the OSG ( Pordes et al., 2007 ; Altunay et al., 2011 ) we mentioned. We also see the potential to share workforce for different research projects if they require it.

Measurement school focuses on standards of measuring metrics of old (like print journals and conferences) and new, mainly internet-based (for example, open access journals, blogs, and social media platforms) publishing formats ( Weller and Puschmann, 2011 ; Priem et al., 2012 ; Yeong and Abdullah, 2012 ). So, for an open science infrastructure, the school demands the capability to calculate old and potentially new metrics to create a measurable environment for the participants. Performance values are substantial for a reputation system and are an excellent possibility to provide incentives in the form of key figures that researchers can improve by their work. The measurement school contains a second requirement that is essential for an open infrastructure; there must be interfaces to connect internal and external systems . In that way, participants have the opportunity to share all kind of data from their own software with the ecosystem and to add new external tools and functions.

4. Blockchain Technology

In this section, we briefly describe the blockchain technology (BT), its characteristics, and functionalities to provide fundamental knowledge about it (section 4.1). After that, we compare the requirements of an open science infrastructure (section 3.2) with the characteristics of the BT (section 4.2). Finally, we present an overview matrix and several examples showing that the technology as a technical basis fulfills the requirements and hence suits as a solution.

4.1. Overview

When talking about BT, the distributed ledger technology needs to get mentioned since it is an umbrella term that includes blockchains as one type ( Benčić and Podnar Žarko, 2018 ). A distributed ledger uses independent systems (nodes) to record, share, and synchronize transactions in a decentralized network ( Kakavand et al., 2017 ). A blockchain works similar but organizes its data into blocks which are cryptographically and chronologically linked together and also may use other kinds of consensus mechanisms and smart contracts ( Anwar, 2019 ). Haber and Stornetta did already basic work for the BT in 1991 by describing a cryptographically secured chain ( Haber and Stornetta, 1991 ), and in 1993 they and colleagues improved that idea with certain functionalities like timestamping ( Bayer et al., 1993 ). Their design still had some flaws, for example, the double-spending problem ( Chohan, 2018 ) and the need for a trusted party for validating all transactions.

In 2008 a pseudonym “Satoshi Nakamoto” released a whitepaper about a novel peer-to-peer-based digital currency called “Bitcoin” ( Nakamoto, 2008 ) that overcame these flaws. Finally, the Bitcoin network went live in 2009 and had a wild journey in the context of its market value (short time over 20,000$ 16 ) and media relevance. It gained most popularity through the high number of news about its value development. We refer to a Wikipedia article 17 that contains numerous sources to reconstruct the detailed history of Bitcoin. Since 2009 many more cryptocurrencies have been developed (so far over 2,000 different currencies) and BT got noticed as a technology that not only can provide an infrastructural environment to manage currencies but also it is enabling the realization of much more use cases ( Casino et al., 2018 ). Due to the possibilities, a research offensive started a few years ago by researchers from all over the world to analyze the use of BT in many different areas ( Casino et al., 2018 ).

The BT does nothing new in a perspective of its single elements, but as a bulk, these elements (for example, decentralization, immutability, transparency, and cryptographic hashing) are unique and avoiding the double-spending problem ( Nakamoto, 2008 ; Beck et al., 2016 ). A blockchain network works without a centralized server. Transactions made in such a network are verified by the decentralized nodes (user systems) ( Abraham and Mahlkhi, 2017 ; Zheng et al., 2017 ) and stored in so-called blocks with a timestamp ( Gipp et al., 2015 ; Lin and Liao, 2017 ). The size limit of blocks can differ between varying blockchains. The blocks are getting linked in chronological order because every one of them (except the first “genesis” block) contains the cryptographic hash of the previous one, so they form a chain ( Beck et al., 2016 ; Crosby et al., 2016 ). The block hash considers not only structural data of a specific block but also its content like, for example, transactions.

It depends on the blockchain whether users can store complete files on-chain or they need to use off-chain solutions like a cloud or an InterPlanetary File System (IPFS) ( Benet, 2014 ) due to file sizes. An IPFS is a peer-to-peer distributed file system for storing and sharing data. It connects computing devices with the same network of data, and each device holds and distributes a portion of the overall data. In relation with a blockchain, the chain only stores an associated hash that references to the actual file on an IPFS. Note, that off-chain solutions (sometimes referred to as “second-layer” blockchain solutions) introduce new challenges and are an interesting research topic on their own, but one that goes beyond the scope of this paper.

In general, a blockchain is a type of database that only supports reading and appending ( Swan, 2015a ; Yli-Huumo et al., 2016 ). Due to its decentralized architecture, it operates as a peer-to-peer network, so users (peers) are interacting directly with each other without the need of trusted intermediaries or authorities ( Hoffmann, 2015 ; Catalini and Gans, 2016 ; Christidis and Devetsikiotis, 2016 ) calls it “trustless trust.” Participants that trade with each other make an agreement for transferring, for example, physical or digital assets ( Casino et al., 2018 ). The nodes of the other users in the network are then verifying the transaction by the programmed rules of the system to make sure everything is valid before it gets executed ( Nakamoto, 2008 ). The verification is essential because all records and transactions in a blockchain are immutable (tamperproof) ( Gipp et al., 2015 ; Zyskind et al., 2015 ). The consensus mechanism of the network is responsible for how verifications for the users are working. As an example, we mention the consensus mechanism Proof-of-Work (PoW) ( Nakamoto, 2008 ; Tschorsch and Scheuermann, 2016 ) which is, among other blockchains, used in the Bitcoin network and is the best-known method, but heavily criticized for its high energy consumption ( O'Dwyer and Malone, 2014 ). Another one is Proof-of-Stake (PoS) ( King and Nadal, 2012 ; BitFury Group, 2015 ; Tschorsch and Scheuermann, 2016 ; Zheng et al., 2017 ) that offers a more efficient way for verification and consensus finding, in terms of energy consumption and performance.

Literature categorizes blockchain networks in terms of their access and governance system into the following different types: public, private, and consortium, which is also called federated ( Buterin, 2015 ; Swanson, 2015 ; Kravchenko, 2016 ; Zheng et al., 2017 ). Additionally, they become separated in perspective of their consensus process into permissionless and permissioned infrastructures; these are getting combined with the various blockchain types. In public (permissionless) blockchains like Bitcoin, everyone can join and participate in the system. In private and consortium (permissioned) blockchains, only users have access who are on a whitelist; typically, parties that know each other. Other combinations of the types and consensus process permissions are also possible. For more information and a comparison between the different kind of blockchains, we refer to Zheng et al. (2017) and Casino et al. (2018) .

Application programming interfaces (APIs) are essential for a blockchain to connect off-chain (external) hardware and software with the network. It enables communication as well as the transmission and exchange of data between the systems ( Linn and Koo, 2016 ; Liang et al., 2017 ). In such a way, external applications (including web-services, Beck et al., 2016 ) can integrate the characteristics and functionalities of an existing blockchain for specific use cases ( Linn and Koo, 2016 ; Xu et al., 2016 ). For example, it is possible to hash and store research data directly from external sensors, algorithms, and other data creating processes. So, an API is an important feature of a blockchain in terms of interoperability that developers always should provide and document to maximize the blockchain's potential and ease its use.

BT has developed continuously; Swan (2015a) describes three evolutions (Blockchain 1.0, 2.0, and 3.0) that led to new possibilities of using the technology to realize steadily more complex applications and projects. Ethereum ( Buterin, 2014 ) is a blockchain application that provides an infrastructure, comparable to an operating system, which everyone can build their applications on top without the need of the cost-intensive development of an own blockchain. Ethereum introduced smart contracts (SCs) that are programmable in specific languages, for example, Java, GO, and Solidity ( Dannen, 2017 ), and allowing for the automatic enforcement of a digital contract with typically if-then clauses ( Bhargavan et al., 2016 ; Christidis and Devetsikiotis, 2016 ; Kosba et al., 2016 ). There are even projects to create complete decentralized autonomous organizations (DAOs) to automate organizational governance and decision-making with SCs ( Swan, 2015b ; Jentzsch, 2016 ).

We noticed that there are slightly different characterizations of BT in the literature ( Aste et al., 2017 ; Puthal et al., 2018 ; Treiblmaier, 2019 ; Viriyasitavat and Hoonsopon, 2019 ). Therefore, we summarized the properties of the technology and made the following compressed list of relevant characteristics concerning the open science use case.

• Decentralization : A blockchain is a distributed redundant peer-to-peer system of nodes each storing the whole blockchain or a part of it ( Abraham and Mahlkhi, 2017 ; Zheng et al., 2017 ). The architecture even allows for distributing software and other content through the network automatically ( Kiyomoto et al., 2017 ). Further, decentralization also eliminates a potential single point of failure and removes the dependency of a central authority that has to be trusted ( Kshetri, 2018 ).

• Cryptographic Hashing : Due to the hashed connection embedded in every block of a blockchain to the previous block, a chronological chain gets created ( Nakamoto, 2008 ; Gervais et al., 2016 ). Besides the consensus mechanism, hashing ensures that the complete chain, inclusive the content, cannot be altered because a change would affect one specific hash value, and from there one, all subsequent hash values, and hence the chain would get invalid ( Zheng et al., 2017 ). It also allows generating a unique hash of files of any size to create an identifier. For more information about the hashing process, see the following references ( Zain and Clarke, 2007 ; Nakamoto, 2008 ; Lemieux, 2016 ).

• Timestamping : Every record (block creation, transaction, data storage) in a blockchain gets chronologically timestamped. It provides traceability, transparency, and full transaction history for the users ( Nakamoto, 2008 ; Gipp et al., 2015 ; Mattila, 2016 ; Zheng et al., 2018 ). Timestamps in combination with a cryptographic hash can also be used, for example, as a Proof-of-Existence for certain information at a particular time ( Gipp et al., 2015 ).

• Immutability (Append-Only) : Data, once stored on the blockchain, cannot be altered or deleted anymore; the cryptographic hashing and decentralized validation (consensus) process ensure that ( Swan, 2015a ; Yli-Huumo et al., 2016 ). Exceptions are specific attacks like the 51%-attack, see Dowd and Hutchinson (2015) for more information.

• Consensus Mechanism : They define how users validate transactions in a blockchain among each other ( Abraham and Mahlkhi, 2017 ; Zheng et al., 2017 ). Since the Bitcoin blockchain and PoW, many new unique methods and combinations of existing consensus procedures got developed and implemented in new blockchains. For more information about consensus mechanisms, see Zheng et al. (2017) , Abraham and Mahlkhi (2017) , and Nguyen and Kim (2018) .

• Access and Governance System : Every blockchain gets also characterized through its access (public/consortium/private) ( Peters and Panayi, 2016 ; Lin and Liao, 2017 ) and governance system (permissionless/permissioned) ( Gervais et al., 2016 ; Peters and Panayi, 2016 ). These properties are crucial to the potential use cases ( Lin and Liao, 2017 ).

Note, that the characteristics mentioned above are not exclusive to BT. As mentioned in the introduction, there exist other approaches that also have one or more of these properties.

4.2. Blockchain Technology as an Open Science Infrastructure

In this section, we compare the characteristics of BT with the needs of an open science infrastructure. With this, we study whether the technology suits as a foundation. Therefore, we made a matrix that shows which characteristics are important for the specific requirements and can meet them (see Figure 2 ). It is crucial to understand the matrix as a whole because several demands and characteristics complement each other. For example, it is useful or needed for many functions like for providing a trail of research that there is no censorship possible in a blockchain network to provide a trustworthy environment ( Swan, 2015a ). Altogether, in this section, we describe, along with different examples, how the specific blockchain characteristics meet the requirements of an open science infrastructure. We do not claim to make a detailed model or concept of an ecosystem.

Figure 2 . Matrix about open science infrastructure requirements and blockchain technology characteristics that are fulfilling them.

In terms of accessibility and governance , we concluded in an early analysis phase that a consortium/private blockchain makes no sense for its application as an open science infrastructure. One fundamental essence of open science is to share knowledge globally and the science process itself plus the results out of it accessible for a broad audience or even everyone ( Bartling and Friesike, 2014 ), without differentiation according to characteristics of any kind. A public blockchain is suitable and can meet that purpose while a consortium/private blockchain would restrict the access. The comparison of a permissioned and permissionless blockchain goes far more in-depth and is connected to different factors like governance models and consensus mechanisms, so it is a research review on its own. In order to gain insight, we will describe two possibilities superficially.

In a permissioned network, the governance is not taken over by all equally, but an organization (we call it committee) must be formed. One possibility could be to democratically elect the members of the committee through a network of universities and research institutions. This committee then decides how the open science infrastructure will develop or what value specific contributions in the network have. The division of roles justifies itself on the fact that non-experts / non-scientists lack the necessary experience to make well-founded decisions in such a system, which is why a permissioned blockchain is elementary with this governance model. So, users get divided into two roles (“user” and “committee user”), which differ in the ability to participate in certain decisions but have the same permissions for all other aspects.

In a permissionless network, everyone is equal in all aspects, but it also opens ways to system abuse. Therefore, a suitable consensus mechanism is mandatory to make collaborative decisions about how the underlying blockchain system is developing and also to prevent malicious behavior in the network. PoW is not the right choice for open science, not least because of its high energy consumption. Instead, more appropriate are mechanisms like PoS, which could be adopted to open science purposes. The distribution of tokens, which are representing voting rights in this system, could be based on scientific experience and merit. How these values are determined and composed would have to be studied in detail beforehand. However, this approach would make the use of a permissionless blockchain possible, since people without a scientific background do not have to be excluded, their impact gets minimized by the size of their stake.

Both approaches have advantages and disadvantages, and it depends on many factors which method is better. There are even more ways to build such a system. A detailed examination of these approaches would be the next step toward a blockchain-based open science infrastructure but goes far beyond the goal and scope of this manuscript. In the following, we concentrate on the comparison of the identified requirements for an open science infrastructure with blockchain features.

An essential topic of an open science system is the possibility to provide a collaborative environment . BT and its decentralization can support that goal by enabling, among other things, all users to share the same data version. In detail, data consists of, for example, experiment results, communication content, drafts, open peer-reviews, and raw data. Also, as mentioned, specific groups or the whole network can make decisions collaboratively through ordinary votes that can follow, for example, a democratic approach ( Osgood, 2016 ). Subjects of these polls could be topics like the future development of the network, to add/remove specific features, or to accept/rate proposed projects and contributions. On a technical perspective, the validation and management of a blockchain infrastructure work as well collaboratively through the consensus mechanism in which all users take part. It also ensures data integrity and consistency in a blockchain.

The immutable (tamper-proof) nature of the BT is an ideal feature to fulfill the requirement to prevent censorship of any kind. As we described in section 4.1, cryptographic hashing , a consensus mechanism , and decentralization in combination guarantee the immutability of a blockchain. Participants of a network can only append data but not modify stored data. This property suits to science that should not underlie any censorship . Everyone should be able to freely express his or her opinion without getting restricted in any way. In the use case of research, it also includes the publishing of scientific work that has critical statements or topics. Overall, an open science infrastructure based on BT can provide such a censorship-free environment.

Considering data created in scientific work, we follow an approach that the data should be open for reuse with appropriate credit to the originator(s), but in reality, often a third party holds the rights for its usage ( Dulong de Rosnay, 2006 ). A blockchain-based open science network with interfaces for data import/export can serve as a solution while the contributors themselves can decide every time to publish their files for sharing and reusing ( Open Data ). In such a case cryptographic hashing plays an important role, so the originator(s) can integrate a hash value that is formed from the content itself and also the names of the authors and other meta information before the data gets published; in that way, they create a digital footprint. It prevents that someone falsely claims and obtains credit for work that other people did ( Dansinger, 2017 ). For additional security, it is always possible to check over a blockchain network the source and time of the creation of certain content ( trail of research ).

In addition to Open Data , an open science network shall represent an Open Access repository of knowledge which means in respect to the open principles that there should not be paywalls that hinder the people from acquiring knowledge for themselves and the scientific progress. A large spread of research works can also contribute to more citations and a better reputation for the authors. Paywalls are technically possible and implementable with BT, but considering the open principles and requirements we determined in section 3.2, we suggest not to integrate any to preserve a real public character. As with Open Data , of course, every participant and group must have the possibility to decide for themselves about the accessibility of their work; hashing can also be used here to create digital footprints.

To accurately reflect the reputation of researchers, an identity and reputation system is indispensable. It creates an incentive for network participants in the form of acknowledgment for their work. As a kind of database, a blockchain fits to function as an identity register to securely store pertinent user data. Each participant can upload content and contribute to the network. Therefore, a rating mechanism is mandatory to measure the quality and impact of stored data. Finally, the reputation of the participants is determinable. In detail, it shall enable all or only specific users to review and rate contributions, for example, papers and certain data like experiment results and micro-contributions to ascertain their value; Casati et al. already proposed an approach how crediting of micro-contributions may work ( Casati et al., 2011 ). Decentralization and the consensus mechanism of a blockchain network ensure that no central authority controls the data and so reputations will be created naturally and independent through the network participants and their feedback.

Similar to ResearchGate 18 and other platforms, the identities and lists of contributions must be accessible by everyone to achieve an optimal recognition of the researchers and their work. These platforms can use such a blockchain-based open science infrastructure as a shared database to access and display identities and metrics. A search engine and filters can help to guide through the users and data to find possible collaboration partners and citable work in different research phases. Linking every account to a real person allows creating a research curriculum vitae that shows the chronological research history of an individual along with all positive (prizes, awards) but also negative (proved plagiarism) milestones. An interesting optional function would be anonymous publishing that allows researchers to work on controversial or critical data and topics without the fear of negative consequences like discrediting. The user name expanded by some random characters can get hashed to prevent any traceability and create a pseudonym for publishing.

Technical infrastructures need to be sustainable. A key factor here is to provide extensible systems . The possibility to expand a blockchain is equivalent to other systems; for example, APIs are enabling to link software with an ecosystem. So, it is possible to communicate with external software and platforms to exchange all kind of content but also to use web-services and functionalities from them. Thus, the range of application scenarios can be steadily expanded. An additional reason is the speed of how technologies are developing today, which makes it crucial to provide opportunities to easily extend existing systems to avoid the need for the time and cost-intensive creation of new software. The consensus mechanism of a blockchain also plays a role since the majority of authorized users must accept a system change so that it gets implemented.

Incentives for using a network are fundamental to motivate people to join and participate. BT can provide different factors to create incentives for an open science infrastructure. One of them is security created by blockchain-based proofs (for example, Proof-of-Existence) and the trail of research that are allowing to support intellectual property protection and to determine who contributed certain content (papers, results, and supporting data) to a network. Note, that timestamping services like, for example, OriginStamp ( Gipp et al., 2015 ) do not protect intellectual property, rather only prove that some person possessed some information at a certain point in time. The protection only gets supported if research objects are timestamped immediately after creation and continuously as they change so that no one else can obtain the information beforehand. Additionally, the creation process can get traced by several timestamps.

Another incentive is decentralization that makes sure everyone has the current version of all data hence assists the dissemination of published work. People are expecting a kind of counter-value when they contribute knowledge what shall be satisfied through the access to published content of others. Another positive aspect is constructive feedback of the community for the provided material (scientific self-correction or open peer-reviews). The technology can further provide monetary incentive systems that use coins/tokens as a reward for contributions; a consensus mechanism can serve as technical implementation (more on this in sections 5.2, 5.3). So, on a technical level, BT offers the possibility to create new forms of reputation and incentives. However, they are worthless if not getting accepted by the respective target groups. The analysis presented in McKiernan et al. (2019) shows that a big part of universities and research institution rely on and trust in well-known metrics like the Journal Impact Factor (JIF) and use them in reviews, promotions, and tenure documents. It is improbable that they will supplant well-established metrics by novel BT-based approaches in the near future.

A network can offer equality of participation and governance model decoupled from a central authority, which is aligned with the open principles. That, in turn, may result in a rise of an incentive to use an open science ecosystem. Basically, in such an infrastructure, all people shall have access to science and experience the same chances to gain knowledge and improve themselves. Decentralization and an appropriate consensus mechanism can technically make sure that the users do not get differentiated by country, race, wealth, level of education, or any other characteristics. If needed, the BT is also able to manage different user roles, for example, to form a committee that collaboratively decides how a blockchain develops as we mentioned in this section.

Besides the incentives that should motivate people to use a network, it also must be simple to integrate its capabilities and services into existing workflows and external software without serious effort or costs. If that is not the case, potential users will likely refuse the system upfront. We think it should be mainly a one-time effort. Proper documentation, a sophisticated network design, and an individual easy-to-use API are essential to ease the integration of subsystems. Also, the consensus model is a relevant factor since it partially defines how much resources of storage space and computing power are needed to participate in the network. In the end, the users shall still use their familiar software to manage their projects and data but with the possibility to benefit from the provided features of a blockchain-based open science infrastructure.

In terms of sharing data and content , a blockchain guarantees that there is no single point of failure due to its decentralized characteristic. So, there is no potential data loss, and the network ensures availability as long as the connection to it exists. For storing new data in a blockchain, a consensus mechanism should validate all incoming files to avoid, for example, dangerous software like viruses or redundant data; a blockchain itself gets already redundantly stored across all users. In the perspective of content management, all originators should have the opportunity to restrict access to their content for whatever reason. Then data gets stored encrypted in a blockchain, so it is not accessible until its owner makes it open to other users; off-chain storages like a traditional database or an IPFS are connectable and usable via APIs as well. In that case, a blockchain only stores the associated hashes of the contents. We also see potential in sharing specific software licenses via an open science network, for example, to optimally use multi-user licenses.

A growing economy is crowdfunding that gained much popularity through platforms like Kickstarter 19 and GoFundMe 20 . Such a crowd-driven method also contains the potential for science to raise money or resources to realize promising research projects ( Swan, 2015a ; De Filippi, 2016 ). BT can offer a consensus controlled monetary coin/token system to allow users supporting projects of their choice. Another option is the connection of external payment systems like PayPal to enable people to invest through traditional digital ways. Concerning identities, hashed pseudonyms offer the possibility for anonymous participations. As an extension, SCs can serve to manage crowdfunding projects, for example, to distribute funds in complex subprojects, to perform votes, or to execute automatic orders and other digital actions.

Another promising element that BT can provide in an open science infrastructure is the ability to create a trail of research that chronologically shows how research objects develop. Timestamping all contributions from scratch (idea, study design) up to the finished paper allows to transparently store all transactions with related hashes in a blockchain and hence to reconstruct the research process in order to improve the reproducibility ( Benchoufi and Ravaud, 2017 ) in science and the acknowledgment of researchers. Contributors can get steadily and immutably linked to their data no matter if it is an idea, a new draft, or a finished paper. The tamper-proof property of the BT ensures that the trail cannot be changed subsequently. If uploaded data has to be changed, for example, because of mistakes or updated content, it is possible to add new versions while the old files can get marked or archived; realizable over the front-end (software or website surface) or potentially the consensus model .

Non-experts can also participate and provide valuable data in research (called citizen science ), especially in larger data collections that are consisting of simple information. In a blockchain-based open science infrastructure, participants can use digital sensors for measuring all kinds of properties and benefit from the unique characteristics of the environment. The measurements are automatically getting stored in a blockchain, so tampering or censoring is not possible ( Wortner et al., 2019 ). Sensors can produce storage-intensive data, in that case, a blockchain allows storing hashed data sets as identifiers that save a lot of space, and the associated measurements can get stored in a traditional database or an IPFS instead. Further, timestamps can complement and additionally validate time-related values like temperatures. Finally, the decentralization ensures that there is no central authority or system that users need to trust; data is always available (no single point of failure). The reuse of acquired results enables other researchers to make additional insights and to give feedback. So, they do not need to make another time-consuming/costly experiment to gather already existing information.

As an important requirement for an open science infrastructure, the source code and tools should be open and hence transparent for all users, so they can precisely understand what the algorithms and tools are doing. Openness provides trust, but also the advantage that all participants of a network can collaborate in its development and make or suggest ideas, plugins, and updates to steadily enhance the underlying ecosystem. That also involves prototypical software from researchers for their projects, so experienced programmers can help with feedback to achieve the best possible solutions. If users do not want to make their code or tools accessible, they must additionally have the possibility to encrypt them. The combination with a blockchain allows using its decentralization and the trail of research to support the management and traceability of open source projects.

Besides citizen science and individual contributions in a blockchain-based open science network, people can also participate in research by sharing their unused resources like storage space or computing time of their systems (for example, computers/servers) for scientific purposes. The decentralized peer-to-peer architecture of a blockchain provides an optimal ground to efficiently allocate resources to share them ( Vishnumurthy et al., 2003 ); a consensus mechanism can support the fair distribution. Developers may disseminate their algorithms in a corresponding network, so a multitude of systems (nodes) with different configurations tests them. Such a procedure suits to verify the stability of certain software and to prove that an algorithm delivers precise results. So, researchers are potentially able to run experiments that they could not do on their own, for example, because of a local lack of resources.

Metrics are an inherent part of science and can express, for example, the impact factor of researchers or publications and also show the rankings of conferences and journals ( Van Noorden, 2010 ). They can further serve as a factor for funding bodies to decide to whom they give their resources like in application procedures for specific research topics. We see a blockchain as a great possibility to calculate accurate and reliable metrics for all scientific stakeholders by providing and sharing of a trustable open infrastructure. BT can achieve that through decentralization and the consensus mechanism , so every node in a network participates in the calculation and verification of the key figures. Essential for the qualitative determination of metrics is the complete data foundation. As an example, a personalized impact factor shall cover the full range of a scientist's contributions. However, BT can only help to calculate and validate metrics but does not answer the question of which figures are relevant and meaningful for an open science environment. The current research metrics are a very topical and much-discussed topic ( Brembs, 2018 ; DORA, 2019 ).

The last requirement is about using connected systems that are not only beneficial for metrics. They are also useful to ease the exchange of all kind of data like experiment results, study designs, and papers. With particular APIs, it is even possible to automate the file distribution across system boundaries. For instance, if researchers store a file in their local storage, its dissemination could automatically take place in a connected infrastructure if desired. It behaves similarly with communication; users can send messages from one to another network. Such functionalities are supporting the integration of a blockchain-based infrastructure into external workflows and reducing the effort to work in two or more systems.

At the end of this section, we would also like to point out that the realization of a scientific platform is often made difficult or impossible by the lack of consistent funding. These are long-term projects that require detailed and well-considered preliminary planning and cause costs not only for development but also continuously for maintenance and expansion. Blockchain-based infrastructures also face this difficulty, but with the possibility of providing incentives such as cryptocurrencies that can create speculative value for investors. Thus, people outside the scientific environment get also addressed, but with this type of funding, called initial coin offering (ICO) ( Conley, 2017 ; Li and Mann, 2018 ), science and business inevitably merge. Two examples with scientific background are EUREKA 21 and Scienceroot 22 . The further investigation of ICOs for this purpose should be considered in the future, when the hype about BT has flattened, in order to get a realistic picture.

Altogether, in this section, we answered our first research question and described how the characteristics of the BT can fulfill the requirements of an open science infrastructure and provide many advantages regarding replication of results, transparency of research processes, and also the traceability of research objects. The current technological state is already capable of the realization of such a platform. Nevertheless, a variety of general and technical questions in terms of a suitable consensus and governance system, incentive factors, law, and data storage still have to be answered in future research work; we explain some of these issues in more detail in section 6. Current literature and projects are focusing on different goals, a few of them describing specific use cases like resource sharing, publishing, and especially reproducibility. More are following visions of holistic science platforms that are offering different functionalities to support research. Therefore, we will analyze the state-of-the-art in the next section to answer our second research question and overview what literature and projects are already available or in development and what is the current state of the BT for open science.

5. State-of-the-Art

This section starts with a description of how we analyzed the current state of research and how we categorized relevant blockchain projects to clarify our approach (section 5.1). After that, we give an overview of available literature (section 5.2) and projects (section 5.3). Finally, we summarize and discuss the state-of-the-art (section 5.4).

5.1. Research Overview

To create an outline of the current research, we have read and analyzed research papers, concepts, and applications up to April 2019 that are connecting BT and open science or are relevant in other forms to this topic. Currently, there is not much pertinent literature, but the amount is growing, suggesting that this research subject is in an early phase. Since there is little literature, it would not make sense to structure it. It is different with practical blockchain projects, of which we finally examined 60 in detail: 18% in a concept, 52% in a prototype, and 30% in a deployed status. We assigned each project to one of the six categories shown in Figure 3 to provide a structured overview of the current research situation. Some of the projects can also offer functionalities that are useful in other categories than their assigned one.

Figure 3 . Overview of categories of open science-related blockchain projects. The figures in brackets show the number of projects in the respective category.

The category Reproducibility contains projects that aim to improve the replication rate in science and so the quality of research. Resource sharing focuses on functions to share unused resources, for example, storage space and computing power. The category transparent evidence mainly revolves around proof keeping like Proof-of-Existence to prove that information existed at a certain time and was in possession of a specific person or Proof-of-Submission of manuscripts to journals or conferences. Projects with the classification of intellectual property protection focus on the protection of ideas, contributions, data, and everything an individual submits to make sure to give appropriate credit to the originators. Social Research Platforms/Repositories feature a multitude of science-related functions like communication, data storage/processing, reputation, and identity mechanisms; most projects fell into this category. Customizable infrastructures allow building individual solutions on top of existing blockchains to prevent the effort and costs to develop a custom blockchain.

In total, we investigated 83 projects (see Supplementary Material ) and excluded 23 of them because they provided insufficient information, were not mature enough to improve a scientific aspect, or were inactive/canceled. Most of the remaining projects have in common that they use BT to enhance different factors and elements of research, for example, trustability, workflows, transparency, reproducibility, and collaborations. The others offer specific mechanisms that are promising for improving processes in science. In order to show the current capabilities of the BT for open science, we will describe relevant literature (section 5.2) and different concepts, prototypes, and applications (section 5.3) in the next parts of this paper.

5.2. Literature

Since it is an early research phase, there is little literature about open science in combination with BT, but still, there are exciting and promising concepts, ideas, discussions, and approaches that we want to describe and highlight.

Dhillon wrote an article ( Dhillon, 2016 ) and with others a book section ( Dhillon et al., 2017 ) about BT and open science. They start the relevant chapter in their book with the current reproducibility crisis ( Prinz et al., 2011 ; Collins and Tabak, 2014 ; Baker and Penny, 2016 ; Gilbert et al., 2016 ) and the rare publications of negative results ( Matosin et al., 2014 ; Van Assen et al., 2014 ; Mlinarić et al., 2017 ). Dhillon et al. state that the BT has the potential to mitigate the crisis. They use a clinical trial as a practical example and define a workflow making the complete research process transparent while protecting critical data of patients ( Dhillon et al., 2017 ). Also, other publications are proposing the use of BT in the medical or biological area to provide, among other aspects, transparency and trust ( Nugent et al., 2016 ; Benchoufi and Ravaud, 2017 ; Ozercan et al., 2018 ). Further to the research process, Dhillon also proposes to apply their approach to implement a kind of reputation system (with an API) as a reward for researchers and an indicator for the quality of contributions ( Dhillon et al., 2017 ).

Another use case highlighted by Dhillon et al. is blockchain-based prediction markets, where mainly experts try to predict a specific outcome like the potential of reproducibility of an experiment ( Almenberg et al., 2009 ; Dreber et al., 2015 ; Dhillon et al., 2017 ). To create an incentive to participate, users get rewarded for the right prediction, for instance, by monetary coins/tokens of the related blockchain. An article by Extance (2017) contains similar statements saying that the BT can enhance the current replication situation in science, but he additionally mentions the potential of the technology for the peer-review process to build up trust due to immutability and transparency. But also, the article reiterates the statement made by Pagliari ( Extance, 2017 ) who expresses concerns about storing possibly incorrect data in a blockchain that are then immutable. A patent about the usage of BT in open scientific research ( Ahn et al., 2018 ) complies with the open principles and focuses on the integration of the technology into research workflows to allow such a tamper-proof sharing of information to improve the trustworthiness in science.

Bartling manages an open living document about the usage of the BT for open science that contains many promising ideas, projects, and hypothesis ( Bartling, 2018 ). It is special because everyone contributes to the paper by feedback, visions, or suggestions, so a collaborative and constructive discussion can take place about its contents. The statements in the living document are consistent with those by Dhillon suggesting to use the BT for science to enhance reproducibility, collaborations, and trust, but they advance even a step further. Besides many blockchain projects, they also introduce novel ideas for funding research, incentive systems for all kind of scientific activities, and an open repository for data sharing ( Bartling, 2018 ). For example, ICOs can be used to fund research projects ( Conley, 2017 ; Li and Mann, 2018 ). Interested parties (also citizens) can take part in funding and get a consideration for it what could be a later service (like usage rights/licenses) or monetary coins/tokens of a newly generated blockchain.

Statements in the living document criticize the publication bias for positive results because negative outcomes may also be valuable and prevent the waste of time and money that researchers are using for experiments that already failed for others. In that sense, Chen et al. (2018) propose an architecture for blockchain-based provenance sharing of scientific workflows to provide a secure and easy way for scientists to share their research data, for instance, to prevent the waste of resources. Bartling also founded a company “BFS Blockchain for Science” 23 that aims to foster the usage of BT in science, among other things, by organizing conferences/workshops 24 , supporting blockchain projects/startups and upcoming developers with relevant knowledge, and providing new ideas. Dhillon's and Bartling's suggestions match Rachovitsa's statements ( Rachovitsa, 2018 ). She mentions the potential of the BT to implement novel incentive models and to improve the transparency of open data and open access systems while enabling researchers to manage their intellectual property through SCs.

van Rossum (2017 , 2018) also identifies blockchain as a technology that can foster especially open science in many aspects hence corresponding to most of the statements by Dhillon, Bartling, and Rachovitsa. In addition, he highlights that BT can change the role of academic publishers in the future. He notes an increasing commercial interest in science, dominated by a few large publishers who established paywalls around research works to make a profit out of them ( van Rossum, 2017 ). On top, focusing on current metrics can lead researchers to pursue the goal of high ranking rather than primarily doing good research. BT can help to mitigate such problems, but a significant factor that Van Rossum mentions is the adoption rate of the technology by the scientific community and its stakeholders. Acceptance will have a decisive role in the futures development of the technology for open science and other application areas. Another success factor he brings up is the existence of a common communication interface, so a trustworthy collaborative environment gets created.

The report ( van Rossum, 2017 ) of Van Rossum contains two interviews as well; one with Efke Smit 25 and another one with Philipp Sandner 26 . Smit says that we already have a working academic world and puts into question why the scientific community should take the effort and costs of changing to a new system with BT. She summarizes that the technology, whether it is widely established or not, will be probably unnoticed anyway by non-geeks; the future will show if blockchains prove themselves as a game-changer or as a hype. Sandner sees the potential for using BT and SCs in science; as application examples, he mentions funding, publishing, scholarly communication, and incentive systems. Further literature and a web article by Bell et al. (2017) , Brock (2018) , and Opoku-Agyemang (2017) likewise describe the many possibilities of BT to improve science and all kind of research activities as statistical analyses, data evaluations, and medical trials.

Intellectual property is a regular output in science which can be very valuable and should be protected so others are not able to steal it and the originator can appropriately be credited. de La Rosa et al. (2017) analyzed how blockchain-based protection of intellectual property in open innovation processes can work; such an approach is also critical for scientific environments. The safeguarding has to start right at the first appearance of an idea ( Schönhals et al., 2018 ) to provide a trustworthy system and to motivate researchers and other individuals for open collaborations. As a simple example, an idea that appears the first time can be timestamped and immutably stored in a blockchain to prove its existence at a certain time point; also, originators can add metadata like their names to these transactions.

Since most projects we found are social research platforms and repositories that allow their users to discuss ideas and hypothesis openly before they are processed, we see the protection of intellectual property as fundamental. de La Rosa et al. (2017) conclude that the BT can provide great benefits for open innovation processes and the protection of its outcomes; other researchers confirm this in their research papers ( Gürkaynak et al., 2018 ; Rivière, 2018 ). But there is still much to do: it lacks approaches to prevent unauthorized reuse of intellectual property, and most existing blockchain applications are not mature yet ( Schönhals et al., 2019 ). A few more ideas about this topic can be found here 27 , 28 .

Another core part of the scientific process is the peer-review of submitted research work. It is one of the most important activities because not only the acceptance of papers for conferences or journals and hence the progression of PhD students and researchers are depending on it, but also research grants and hiring are related to it. Therefore, reviews need to be neutral, trustworthy, and transparent without any bias to provide a fair chance for all participants in science. But there are some concerns about the fairness and quality of today's review system and the opportunities to abuse it ( Smith, 2006 ; Tennant et al., 2017 ). Most of the time, peer-review is a black-box process, so reviewers are anonymous (authors mostly not), and malicious behavior is difficult to detect. Such lack of transparency can lead to a loss of trust. In that regard, several researchers see potential in the BT to improve and open up the peer-review process ( Spearpoint, 2017 ; Tennant et al., 2017 ; Avital, 2018 ; Jan et al., 2018 ).

In a multi-disciplinary study of Tennant et al. (2017) about innovations in peer-review, they identified the BT as a potential future model with promising possibilities. Examples are incentive systems with coins or tokens that reward the reviewers for their efforts, and authentication/certification methods for fraud control and author protection. They conclude that the technology can enhance the quality and responsiveness of the review process. Both Avital (2018) and Spearpoint (2017) , are independently underpinning these statements by proposing two different blockchain-based systems that use monetary incentives along with new metrics to address inefficiencies of the review process. Jan et al. (2018) are also sharing the opinion and utilized BT and SCs to develop a peer-review prototype.

Tenorio-Fornés et al. (2019) criticize the oligopolistic position of the publishers in academia regarding policies, embargo periods, and restrictions about the dissemination of data and propose a blockchain-based publication system for open science to address that. They say that the BT has the potential to realize the promise of open access with new models of data distribution. Another interesting idea comes from Hoffmann et al. (2018) who are naming their approach Smart Papers, which are SCs that are managing attributions and annotations of scholar publications. They aim to use the trustworthy environment of the BT to provide a framework for collaborative authoring and to implement a web client in future work.

Janowicz et al. (2018) wrote a paper about blockchain-based open science and publishing. They propose an informal model of how to use the BT to enhance and partly automate the general scientific workflow, particularly academic publishing, with the support of SCs. Besides their primary focus, they identified promising use cases of the technology in open science that we partially already mentioned in this section, for example, creating transparency of the peer-review process, storing and tracing all kind of scientific data to foster reproducibility, and connecting researchers to potential investors and vice versa. Moreover, managing intellectual property, democratizing of science for significant decisions in the community, and opening up black boxes like algorithms or closed data.

But Janowicz et al. (2018) also express concerns for the implementation of BT for open science. For instance, retractions are normal processes in science due to mistakes, updated papers, plagiarism, and other reasons but in case of a blockchain data is immutable once it is stored; a reasonable handling for such a use case has to be found. Another critical concern is the question of how and to what extent financial incentives may lead to unintended behavior since quite a few projects are using monetary aspects as motivation for their users. There is a chance that the focus of researchers could shift from actual research and knowledge creation to an economic mindset what should not be a major driver in science. Finally, Janowicz et al. (2018) criticize the high number of blockchain-based concepts that barely contain precise details to understand their exact workings and value proposition. We also found several projects in our analysis that did not provide enough information to understand their intentions or applications technically so that we can confirm this statement.

5.3. Projects

In the following sections, we describe use cases of the six categories we defined along with associated projects. We do not aim to present every single project in detail as it would be far beyond the scope of this paper; moreover, several of them are similar and follow more or less the same goals. Also, we include some approaches and applications that are not focused on science but contain specific interesting functions or mechanisms that are promising if transferred to blockchain-based research workflows. Our analysis includes projects that are at concept, prototype, or deployed status; some of them are commercial. Regarding references, we preferred research papers or whitepapers. If these were not available, we referred to the related website or GitHub repository.

5.3.1. Social Research Platform/Repository

We classified most of the projects that we analyzed as social research platforms/repositories . Especially in this category, the concepts and applications often provide many overlapping functionalities and have similar goals. Potential use cases are to create open platforms, repositories, or marketplaces to support collaborations in science and to allow open access to research data hence improving the reproducibility of experiments, studies, and other kinds of research. Typically, they contain much more capabilities like communication methods, reputation and identity mechanisms, and incentive systems for their users. Further, the traceability of the BT serves as protection of the contributors and creates a trustworthy and transparent environment. Two exemplary open science platform projects are Frankl (2018) and Aletheia (2018) .

Some blockchain-based projects also aim to open up the publishing process and to provide incentive mechanisms for peer-reviewers in order to be more transparent, trustworthy, and rewarding; they function similar to an open access journal. Examples are Publish and Evaluate Online (PEvO) ( Wolf et al., 2016 ), EUREKA ( EUREKA, 2019 ), and the concept of an academic endorsement system (AES) ( Anonymous, 2016 ) that was published anonymously. The AES paper criticizes specific aspects of the scientific system. Their approach involves, among other features, the possibility for researchers to individually endorse the work of others with a currency of the network. Steemit (2019) , as a non-scientific application supports such a mechanism along with a reputation system so users can independently reward other users for their content/contributions. There are also existing projects that, in addition to features for collaboration, research management, and publishing, also provide funding methods for research, for example, Scienceroot ( Günther and Chirita, 2018 ), the Open Science Network (OSN) ( OSN, 2019 ), the Decentralized Research Platform (DEIP) ( DEIP, 2018 ), and Orvium ( Orvium, 2018 ).

In order to gain more trust and transparency in their fund granting for research, The National Research Council of Canada created a blockchain-based prototype that is named NRC-IRAD ( NRC-IRAP, 2019 ) to proactively publish grants and contribution data in real-time. We think this approach also has great potential for other countries. It works as a public blackboard for researchers and their groups or organizations who can apply for certain government-funded research topics. Making research data workflows FAIR (findable, accessible, interoperable, and reusable/reproducible) with using a decentralized data infrastructure is the goal of DaMaHub (Data Management Hub) ( DaMaHub, 2019 ). Their first implementations combine BT to transparently record and track all system transactions and IPFS for data searching and storage. In case of content dissemination, LBRY (not science-related) ( LBRY, 2019 ) has an interesting approach as a community-operated digital marketplace in which content owners can set individual fees for their contents without any dependence on intermediaries; similar to WildSpark ( Tabrizi and Konforty, 2017 ). Such a method transferred to science may allow researchers to publish, distribute, and potentially monetize their work individually. The system could also be expanded with a peer-review process to create a blockchain-based journal.

Matryx ( McCloskey et al., 2019 ) follows a novel approach and aims to incentivize the collaboration in science to foster the creation of innovative ideas and projects. Besides providing a marketplace for buying and selling digital assets, it also uses a blockchain-based tournament system in which, for example, a user can create an individual challenge with a particular bounty that gets paid off as a reward to the user who solves the problem. An exceptional topic is focused by Space Decentral (2018) that is a DAO whose aim it is to let the network's community in control for deciding how the science space programs on the platform will continue; functions as crowdfunding, sharing of research data, and peer-reviewing are integrated.

ScientificCoin (2018) is a crowdfunding platform that attempts to determine the potential/risk of scientific projects by several different factors in a mathematical algorithm and expert evaluation. Target groups are researchers that are searching for funds and investors. But it also opens up a way of receiving valuable feedback on research projects, which can help to identify and improve planning or methodical shortcomings. Another extraordinary blockchain-based network is Coegil (2019) , which connects decision-makers with the expertise of many people (participants of the network) to eventually being able to make decisions of high quality. Transferred to science, we see the possibility in such a kind of system to get valuable feedback for research works by experts; especially young PhD students can benefit by that in preparation of their first publications.

The project bloxberg ( Vengadasalam et al., 2019 ) provides a blockchain network that consists of several research organizations that form a consortium and administrating the ecosystem. They aim to foster, among other things, sharing of data, collaboration, peer-reviewing, handling of research claims, and publishing with the help of a secure global environment. The bloxberg system also allows using it as a base structure to develop new applications on it. ARTiFACTS ( Kochalko et al., 2018 ) uses this infrastructure to build a research platform that provides indexing functionalities and a dashboard that displays multiple statistics on the stored content of a researcher. So, it is capable of creating a transparent data trail for research objects and determining several scientific metrics; the developers also plan to extend their system with a blockchain-based digital identity network.

A further blockchain infrastructure that focuses especially on the validation of data integrity in biomedical studies is TrialChain ( Dai et al., 2018 ). This idea is also interesting for other scientific areas because data integrity plays a central role in all kinds of studies/experiments. One more noteworthy and ambitious approach is Project Aiur ( Project Aiur, 2018 ), which envisions building an open platform for validated knowledge without access barriers, publication bias, and information overload while all research is reproducible. To achieve their vision, they aim to combine a repository and a community-governed artificial intelligence that is capable of automating knowledge validation.

5.3.2. Reproducibility

Blockchain projects with a focus on reproducibility in science or the potential of improving the replication rate are subject in this category. Furlanello et al. (2017) proposed their PROBO-network, which is an approach to enhance scientific reproducibility with BT. In general, they want to solve the issue of rewarding time and expertise of scientists that are replicating research results by establishing a monetary-based incentive for them. To achieve that a researcher (proponent) publishes, for instance, a timestamped study with all supporting data in the PROBOS-blockchain and deposits a pre-determined amount of probos tokens to broadcast a request to the network, where clients (verifiers) can evaluate the quality of the study and verify its reproducibility; verifiers getting rewarded by the deposited tokens of the proponent ( Furlanello et al., 2017 ). Especially in the medical sector reproduction of results is vital, for example, to produce reliable drugs for living test subjects and the global market but also to build upon promising and robust basics to prevent resource wasting with irreproducible research.

Forecasting and prediction markets like Gnosis (2017) , Hivemind (2019) , and Peterson et al. (2018) are another kind of promising blockchain-based projects. These markets involve people with expertise who predict or confirm specific outcomes based on existing information, representing a concept of collective intelligence. Such systems are usable in many application fields, for instance, in science to support reproducibility. Among other things, its participants can forecast or confirm the replication probability of experiment results. That procedure is suitable to obtain information in a short time to optimally allocate limited resources into reproduction projects ( Dreber et al., 2015 ). The incentive for the users of these platforms usually is of a monetary nature because they get a pre-deposited coin/token reward for correct predictions and confirmations from the creators of requests.

The next blockchain-based project that we want to mention because of its unique approach is Dsensor (2015) even though it seems stopped or canceled. There was no actual news for over a year, and the announced whitepaper is overdue for 2 years, so we assume the project got aborted. It aimed to provide a computational consensus that uses relevant sensor data to determine whether a networks hypothesis is correct or not. So, if a result is measurable and the data access to necessary sensors exists, such a system would be capable of performing an automatic validation/reproduction of a specific outcome and at the same time recording it on a blockchain for securing data integrity.

5.3.3. Transparent Evidence

This category contains projects that intend to create immutable proofs on a blockchain to verify different aspects like the existence of particular information, submission of documents, or time of actions. These digital certifications allow, for example, to support legal procedures and to provide the required security/trust for open technical infrastructures. One project is OriginStamp ( Gipp et al., 2015 ) that offers Proofs-of-Existence in the form of timestamps on the Bitcoin blockchain. So, a person can obtain evidence for being in possession of specific information at a certain time, for instance, documents, results, ideas, and all other kinds of digital assets. Further, CryptSubmit ( Gipp et al., 2017 ) uses OriginStamp as a basis to combine the timestamp functionality with a scientific manuscript management system for journals and conferences. Thus, it creates a Proof-of-Submission that serves as evidence about submission and integrity of data to prevent fraud and theft of research ( Cantrill, 2016 ; Degen, 2016 ; Dansinger, 2017 ). CryptSubmit also supports timestamped peer-reviews to enhance trust in the whole review process and can additionally serve as a basis for open peer-reviewing.

Online discussion and sharing platforms can also use BT to record all platform activities to secure the trustworthiness of messages and data. So, the first appearance of an idea or a micro-contribution gets registered and then is traceable to its originator. VirtualPatent ( Breitinger and Gipp, 2017 ) is a project that proposes such an approach. It aims to function as a social media platform that immediately timestamps every message in the system to allow open discussions about, for example, novel ideas and drafts. PUBLISHsoft (2018) has a similar but commercialized concept and a different target group as it intends to notarize and trace journalistic news; the mechanism is transferable to research data likewise.

An approach that is focusing primarily on the peer-review process in science is Blockchain for Peer Review ( BfPR, 2019 ) that aims to make the procedure more trustable. They envision to extract peer-review data from connected journal management systems to record them in a blockchain hence allowing the reviews to be independently validated. In the following, we describe two non-science related projects with noteworthy functionalities. The first project is Codex ( Codex, 2018 ) that offers its users the possibility to register digital assets. Their platform got designed for art and collectibles (for example, wine and jewelry) where no centralized title registration exists. We see the potential to use such a decentralized register for scientific publications or datasets to prove their existence and affiliation. The second project is Sovrin ( Sovrin Foundation, 2018 ), which is a blockchain-based identity management network. It provides, transferred to research, the technical opportunity to transparently link every contribution to the identities of its originators and therefore to create a scientific curriculum vitae.

5.3.4. Intellectual Property Protection

Since intellectual property is a typical output in research, it is important to protect it and the originators adequately, in special when knowledge gets patented and monetized. The projects in this category are focusing on notarization, licensing, and certifications of digital assets. These systems are usable in many application fields, but one of the most substantial is science. An already deployed and commercialized application is Bernstein ( Barulli et al., 2017 ) that aims to be a notarization service powered by BT. Its underlying system can issue ownership certifications of digital assets that get stored in a hashed form on the Bitcoin blockchain; examples are licenses, research papers, and non-disclosure agreements (NDAs). Another blockchain-based project that is additionally providing the ability to create marketplaces to monetize an idea, patent, or different kinds of intellectual property is po.et ( po.et, 2017 ). The Molecule Protocol ( Molecule, 2019 ) is combining open science and BT to build a collaborative market-based platform for discovery and funding of pharmaceutical intellectual property. They intend to connect scientists, patients, and industry to advance drug development in its transparent, secure environment.

A concept named Coalition of Automated Legal Applications Intellectual Property (COALA IP) ( De Filippi et al., 2016 ) aims to be a free community-driven protocol for establishing an open global standard in intellectual property licensing to form a consistent framework and to eliminate the dependence on central organizations. Also interesting for researchers and their contributions is Vaultitude ( Vaultitude, 2018 ) which is a large-scale project whose team is cooperating with international authorities and law firms to establish a blockchain supported Proof of Authorship for the digital assets of their users. The projects Bookchain ( Scenarex, 2019 ), Attribution Ledger ( Prescient, 2019 ), and ChainPrint ( ChainPrint, 2017 ) are concentrating on protecting and publishing intellectual property, mainly documents, books, and creative works. So, their target groups are authors, publishers, and partially printing houses, but also researchers may use such services if they want to disseminate papers, studies, or other writings. In all three cases, the uploaded data gets recorded via blockchain to create an immutable trail of information to provide trust and security before and after the publication process.

5.3.5. Resource Sharing

Resources are limited; researchers are peculiarly aware of that when some experiments are not feasible due to a local lack of materials, workforce, equipment, or funds. In this regard, a blockchain can serve as a distributor to share digital resources like storage space. Specific projects for sharing storage space, for example, to save all kind of research data securely in a blockchain environment, are Storj (Tardigrade) ( Storj Labs, 2018 ), Filecoin ( Protocol Labs, 2017 ), Sia ( Vorick and Champine, 2014 ), SAFE network ( MaidSafe, 2019 ), and Swarm ( Swarm, 2019 ) in which all individuals can participate by providing unused capacities of their computer systems. Despite that network users are storing the information of data owners, they cannot access/read them, only the owners can do that; the projects use different methods for this, among other things, encryption and file splitting. Information in the form of data is also a valuable resource that is digitally shareable. The Ocean Protocol ( Ocean Protocol, 2019 ) pursues such an approach and helps marketplaces to buy and sell mainly artificial intelligence data/services while incentivizing data reusing and sharing with a blockchain-based incentive system. This data can get used as learning material for artificial intelligences, but also can support researchers in their projects.

Besides sharing storage space and data, there are also approaches to share computing power in a blockchain network. We think a method of that kind is promising to enable, for instance, researchers to execute specific demanding computing tasks such as complex simulations. A project that aims to provide exactly this functionality hence to operate like a distributed “supercomputer” is Golem ( Golem, 2016 ). Their approach works with various nodes (providers) which are offering their unused computing power as a resource in exchange for monetary tokens. In general, other network participants (requestors) can use that provided performance to calculate, for example, algorithms, photogrammetry reconstructions, renderings of movies/CGI, and machine learning applications in an associated sandbox environment. The Golem network supports the distribution and monetization of software as well.

5.3.6. Customizable Infrastructure

Customizable infrastructures are serving as a fundament on which developers can build their designed blockchain-based networks. In contrast to custom-built blockchains, the source code gets already provided, and less know-how is needed for their realization. So, this approach saves time and funds, but it is limited in its possibilities because the underlying systems usually prescribe certain aspects like the consensus model and the basic structure of the network. Most of the projects in this category are focusing on private permissioned blockchains that have mainly companies as their target group, but still, universities and research groups can benefit from these infrastructures. Exemplary science and academic-related use cases are data tracking and auditing, education/training of students, project management, distribution of digital assets, timestamping, and the issuance of certifications. Further, it is possible to use customizable infrastructures to partly build similar applications like the projects we mentioned in the sections 5.3.1–5.3.5 but with the advantage that they can get adapted to specific demands. Also, completely new solutions are realizable.

In every case, the requirements of a project need to get evaluated to decide whether the possibilities of a provided customizable infrastructure are sufficient to fulfill them or a custom blockchain application is necessary. If the estimated quality is satisfying, there is no necessity to incur the additional effort for a new development. We found several projects that aim to provide such an infrastructural framework to build blockchains or blockchain-based applications, for example, Hyperledger ( Androulaki et al., 2018 ) from IBM, Openchain ( Openchain, 2015 ), Multichain ( Greenspan, 2015 ), Blockstack ( Ali et al., 2019 ), and DCore ( DECENT, 2019 ). In summary, we see customizable infrastructures as a perfect introduction to the BT to test its potential and suitability for diverse application scenarios and to gather the first experience in their development.

5.4. Summary and Discussion

Our review shall serve as a snapshot of the current research situation of the BT for open science with an additional view outside the box to other applications that offer useful functionalities for that scope. During the last 7 months in that we collected and analyzed practical projects, we noticed that the market is unstable. A few of them disappeared, got canceled with official statements of their developers, or are subjectively dead based on long-time inactivity. In total, more new approaches were announced in these months, so the trend we identified shows a steadily increasing number of active blockchain projects for open science. That development is also retroactively observable over the past few years.

For section 5, we diligently analyzed 35 relevant research publications (gray literature excluded) and overall 60 blockchain-based projects (see Supplementary Material ) with different application areas and classified them into six categories to structure them corresponding to their orientation. Considering the acquired knowledge, we agree that the BT has a great potential to foster open science in various aspects. Examples are a new level of trust into systems and their transparency, traceability of digital assets, higher reproducibility, innovative citizen science projects, creative incentive methods, and a generally improved research quality. Especially the realizable openness of blockchain applications and the tamper-proof recording of all transactions in a system make this technology to a suitable trustless infrastructure for open science.

In the end, a blockchain alone represents a database with a unique bulk of characteristics but without a specific sense. An integrated application like Bitcoin or Ethereum gives a purpose and functionality to it. So, we differentiate between the blockchain and application layer (includes the front-end), which need to correspond with each other to use the technology as an advantage. Therefore, in open science projects, both layers should get designed in harmony following the open principles to provide a cornerstone for a transparent and trustable environment; the prevention of non-transparency and possibilities for malicious behavior is fundamental.

If a researcher integrates BT continuously within the whole research cycle, it can be useful in every phase, also partially for experimenting if it comes to tests of algorithms or evaluation of sensorial data. As shown, there are many varieties of using the technology in science to achieve a win-win situation for all stakeholders. In combination with sophisticated application design and development, it is also able to enable new usage models regarding research management, peer-reviewing, funding, and publishing. However, the expectations must be realistic; BT is not a cure for all existing problems in science or an all-in-one solution.

During our analysis, some questions and concerns arose in terms of various projects and other aspects that should get examined in future works. Below, we will briefly describe these uncertainties; more details to the most relevant topics will follow in section 6 to answer our third research question. Many projects are introducing own incentive methods that are often of monetary nature; examples are bounty systems or coin/token rewards for specific actions. On one side, we question if it is a suitable approach to integrate such financial aspects in the research process. Would that shift the intention to create knowledge and progress in science to an economic focus? On the other side, we agree to establish new incentives for the invested time and expertise of scientists who are reproducing and confirming results/studies and peer-reviewing submitted research work for conferences and journals. Further concerns are about how to deal with bugs in already deployed hence immutable SCs, and how different nations are assessing proofs issued from a blockchain in their juristic processes.

The literature and projects also showed that a standard is missing that sets a framework for how blockchains can communicate with external software through APIs, and how data is exchanged to ease the development and integration of the BT into existing workflows. The current situation makes it difficult to identify serious blockchain-based applications. The enthusiasm around this technology led to many new project announcements in the last few years, but in the area of open science, most are in concept or prototype status as our analysis showed hence are not suitable for full integration. To prevent the waste of resources, we advise making sure only to actively use blockchain applications that are at a mature state and already providing the desired functionalities. Due to the unstable market, projects can disappear from 1 day to another, specifically because most of the time, startups are developing them that usually do not have a financial buffer.

A couple of the analyzed projects aim to make intermediaries in science obsolete. These would primarily be publishers. However, the publishers can also use the BT for their good. It provides the potential for them to partially automate distribution and peer-review processes via SCs, and to decrease their costs to manage the steadily increasing amount of knowledge and number of publications. As a synergy effect, these aspects can also be positive for researchers, for instance, through fewer publication fees and faster feedbacks. Further, publishers can open up their operations to transparently show how peer-reviewing and other activities function in order to improve their trustworthiness.

Funding bodies as one stakeholder group in science are using, among various factors, metrics for their decisions on how to distribute their financial resources to researchers and their projects. The problem is that indicators of the same researchers and publications are often differing from one research platform to another due to the circumstance that they use different databases to calculate their key figures. We think the basic technical structure of a blockchain is an excellent opportunity to create a shared, transparent storage. So, it can provide the same data for every science platform to calculate precise metrics like the impact factor of a researcher or a publication.

We also think, as mentioned in some literature, that the adoption rate of the BT will decide about its future development both in science and in all other application fields. So, the number of users is a key factor; a network without participants does not make sense. Most of the projects we analyzed had, from a subjective point of view, a non-existent or small community, so we opine that the technology needs a push explicitly for its usage in open science; maybe a big publisher, stakeholder, or a norm? Overall, it is still a fairly new technology, so it is not yet possible to say for sure how the masses will interact with it and what behavior will emerge.

In this section, we answered our second research question and gave a picture about the current research state of BT for open science along with its possibilities and uncertainties that we identified during our review.

6. Challenges and Research Potentials

In this section, we describe in the context of our third research question challenges and research potentials that we identified during our analysis. Future works should address them in order to eliminate technological and legal insecurities and to enhance the usability of the BT for open science and beyond. We focused on some of the most relevant and promising topics in our view, which got not or insufficiently investigated yet. They shall provide an impulse in the form of starting points for further research; as a positive side effect, addressing these issues can partially also foster other non-scientific areas.

We want to point out that the challenges presented in this section are very complex and profound, so we do not expect them to get resolved in the near future. For example, the correctness problem of software which is fundamental to smart contracts (see section 6.1) is around since the early days of programming, and till today a solution is not yet in sight. Therefore, the following topics are an outlook into vital pillars that need to be considered in the course of a broad integration of BT.

6.1. Risks and Validation of Smart Contracts

Trustworthiness is a key element of BT and one of its main drivers, so developers should design all aspects in their applications in a way to support and provide that property. In this regard, we see SCs that get used in many projects as critical because they can offer various possibilities for malicious behavior and are prone to crucial coding errors in their development. The ability to use Turing-complete programming languages opens up not only numerous use cases and functionalities but also increases the complexity and thus the potential for human mistakes and the number of backdoors/exploits. These can cause, for example, crashes of the processes or vulnerabilities of the program itself that may allow hackers to steal the resources that a digital contract manages ( Bigi et al., 2015 ; Atzei et al., 2017 ). The novelty of SCs justifies the circumstance that the common knowledge about their design, implementation, programming, and validation is not well developed yet.

One approach to counteract vulnerabilities of SCs is to limit the expressiveness of the underlying programming language ( Dannen, 2017 ). Another possibility is the several commercial providers of audit services that have got founded in the last years. They are checking SCs to make sure they fulfill their purpose without eventual weak points. Examples are Runtime Verification 29 and Securify 30 . In that sense, we see research potential in investigating ways to automate the formal verification of SCs through software to quickly eliminate the possibility of specific attacks ( Bigi et al., 2015 ; Luu et al., 2016 ). A further approach can be a modular construction kit to be able to build digital contracts piece by piece for reliable, simple applications. Hence no great coding skills are required, and the creation process gets eased, similar to OpenZeppelin 31 . Also, standards can generally improve the design procedure and security. There is still much to do on this topic to enable an efficient and secure large-scale use of SCs for all application areas.

6.2. Missing Standardization and Frameworks

Established standards and frameworks for technologies can be vital and bring several advantages with them like time-saving, error prevention, and increased security. Through our analysis, we have concluded that these are largely absent in BT. So far, blockchain developers have taken a pioneering role and mostly programmed their applications in different languages without technical specifications. Thus, many unique application structures emerged that have their advantages and disadvantages as well as security risks and vulnerabilities. Standards for BT can help to foster its adoption, interoperability, make systems more secure, in particular, build trust ( Deshpande et al., 2017 ). Also, they enhance the accessibility into the general development of blockchain applications. In terms of software communication, standardized APIs can make the design of new interfaces redundant in most cases.

There is still a lot of potential in researching suitable standards and frameworks for the BT, for example, to ease the design and development of blockchain-based software, or to integrate a blockchain into research workflows. Also interesting are unified methods of how academic publishers can use this technology to improve certain of their processes and benefit from it. In our opinion, infrastructural frameworks like Hyperledger will play an even more prominent role in the future in creating a variety of new applications. One general goal of standards and frameworks must be to facilitate the entry into blockchains in order to address non-experts and break down access barriers. Altogether, both topics offer a lot of promising research possibilities, and we think they will be a cornerstone of the BT in the future.

6.3. Incentive Systems for Science

We noticed that several of the blockchain projects in our evaluation are using diverse monetary incentive systems that function through the issuance of digital coins/tokens for research contributions or specific actions like peer-reviewing. We question these incentive methods due to the current instability and speculative nature of cryptocurrencies. The worth of blockchain issued coins/tokens can vary significantly in a short period; there is also a chance of a total loss. Market development of cryptocurrencies is reviewable on Coinmarketcap. Moreover, it is not clear from where the funds are to come. Some projects propose the researchers themselves as funding bodies, but it is questionable whether they will independently reward others for their scientific contributions. Also, such a monetary incentive depends substantially on the amount of funds. Further, we see the chance that financial inducements can shift the focus of scholars from qualitative knowledge creation to a quantitative performance mentality in which they aim to achieve publications as fast as possible to profit economically.

We think there is plenty of research potential in analyzing blockchain-based incentive systems that are reliable and sustainable on the one hand and motivating for scientists on the other. In our view, exciting research questions are how to influence creative performance positively by extrinsic work stimuli, and whether BT can contribute something meaningful to that goal. A further approach is to evaluate existing incentive systems for their improvability with that technology. Currently, incentives in science mainly revolve around metrics such as the number of citations, the impact factor, and the resulting reputation. Another possibility for research is to work on inducements for the increasing quantity of micro-contributions that should also be appropriately getting acknowledged. Overall, there are several starting points worth to investigate to use the technologies' potential regarding the creation of new and enhancing of existing incentive systems for science.

6.4. Scientific Metrics

The primary information sources of scientific metrics are research platforms, for instance, ResearchGate, Mendeley 32 , Altmetric 33 , Web of Science 34 , and Google Scholar. Each of them uses its own database, which consists mainly of research profiles, publications, and their references to other research work. One exemplary metric is the number of citations that is, among other things, an element to calculate the impact factor of research papers and researchers. In that regard we compared, as short examples, the overall quantity of citations of two researchers (Jöran Beel from the Trinity College Dublin in Ireland and Melanie Swan from the Purdue University in Indiana, United States) and of the Bitcoin Whitepaper between ResearchGate and Google Scholar - date: 20th July 2019 (see Table 2 ).

Table 2 . Exemplary comparison of citation metric on two different scientific platforms.

The comparison showed significant discrepancies, and we noticed that they are even bigger with other platforms. Scientific metrics can, for instance, serve as a factor that funding bodies use for their decisions. As exemplarily demonstrated, a problem of this decision-making method is the crucial deviation of the indicators from one to another research platform triggered by utilization of different calculation formulas and a separated database per system. In concrete terms, the decision of a funding body to support a specific researcher or group can turn out differently depending on the examined network because of the non-identical values of the metrics. We think BT is a suitable possibility to noticeably improve the informative value and reliability of the scientific key figure system.

A blockchain as a shared database can provide the same data source to calculate normed metrics, so all research platforms expel identical values. Open questions are, for example, how to handle retractions in an immutable environment or who fills the infrastructure with information and manages it. However, such a working system as a fundament also opens the doors for potential novel metrics of which we think can also get usefully connected to incentive methods for researchers. Altogether, the research possibilities of the BT for scientific key figures are great because, in particular, its characteristics are suitable to build a shared database and beyond that to enhance metrics or to create new ones.

6.5. Legal Uncertainties

Some research has already been done on blockchain-based cryptocurrencies ( Ponsford, 2015 ; Gikay, 2018 ), SCs, and DAOs ( Savelyev, 2017 ; Dell'Erba, 2018 ) in connection with legal issues and topics, but there is still a lot of demand for further work and clarification ( Werbach, 2018 ). Several blockchain projects we analyzed are relying, for instance, on timestamps to prove different aspects like the existence of specific information at a certain time or want to issue certificates to verify the ownership of digital assets. A concrete example is the timestamping of a dashcam recorded video ( Gipp et al., 2016 ) that shows a car accident to confirm the moment of the crash and the authenticity of the video along with other details that can be important for the decision of a legal process. The question is, what is the legal status and acceptance when such blockchain-based evidence gets used in a lawsuit? In the case of that uncertainty, we see it as problematic that a few analyzed projects work with promises which are not juridically secured.

Further, SCs are also legally unspecified. For example, what happens if resources managed by them are no longer tangible or lost due to incorrect programming; which party is to blame and how does compensation work? SCs or DAOs can barely cover all possible real-world case constellations within their program code. In this respect, is there a technical or non-technical way to deal with unforeseen events? More questions are how juristic systems should treat SCs compared to traditional ones, and what possibilities exist to secure the contracting parties ( Savelyev, 2017 )? A general challenge is the different laws and courts in every country or state ( Dell'Erba, 2018 ), which mean that a solution that functions in a particular location is unlikely to work in all other places. So, most likely, there will not be a global consensus, but countrywide specifications would eliminate many legal uncertainties. With the increasing importance of BT and its growing adoption, we believe that juridical topics are playing a major role in the future and should be addressed to support further developments.

7. Conclusions

This paper contains an analysis about how the BT can foster open science, a review of the state-of-the-art, and an evaluation of relevant research potentials and challenges for that subject. We identified the requirements for an open scientific ecosystem and compared them with the properties of BT to verify whether they fit together. In that way, we answered our first research question and determined the technology as a reliable and appropriate infrastructure for open science. Nevertheless, we regard BT as just one building block among others and we believe that the ideas behind open science can only be implemented if all pieces are put together in a meaningful way and complement each other. Concerning our second research question, we collected and reviewed topic related literature and blockchain projects to describe the current situation. We illustrated the possibilities of the technology by many practical examples to show its capabilities for scientific workflows. Some of the analyzed projects already offer functionalities that can optimize research processes, but most of them need additional development time to implement their aimed features. For our third research question, we identified several existing challenges and research potentials. With this, we intend to draw attention to various promising and essential research topics that should get addressed to support the further development of the BT for open science.

The combination of well-known characteristics like hashing, decentralization, and immutability makes the BT unique and explains the increasing interest of science and industry in it. Due to the limited literature, open questions, and the number of projects in concept or prototype status, we noticed that the usage of blockchains in the perspective of open science is in an early development phase. Nevertheless, the technology can already make valuable contributions to that area, for example, by improving current workflows of researchers, establishing trust in technical systems and enabling new collaborations as well as mitigating existing problems. One of them is the reproducibility crisis in which BT is not a standalone solution, but in our view, a supportive part of it. But many projects need more time to mature for being beneficial. However, there is still much to do in terms of standardization, governance models, beginner-friendliness, interfaces, security and legal issues, and educational work to fully exhaust the potential of the technology.

So long as the adoption of the BT grows, we expect it to get more mature continuously. In this regard, the addressing of the identified challenges will play a vital role in the future. The current situation is comparable to a greenfield in which no specific constraints exist, and researchers have many opportunities to implement new innovative blockchain-based systems and application scenarios. Altogether, after our review, we summarize that the capabilities of the BT for open science are by far not exhausted yet. We conclude that the technology can have a significant positive impact on scientific work and its open ecosystems but that primarily depends on the technology's acceptance of the scientific community and all other associated stakeholders, which is currently unpredictable.

Author Contributions

SL has elaborated the entire content of the document, carried out the analysis, and contributed ideas to the topic. The writing of the manuscript was mainly done by SL supported by SS. MS and BG provided critical feedback and helped with the finalization.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbloc.2019.00016/full#supplementary-material

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Keywords: blockchain, open science, infrastructure, ecosystem, review, research potentials, requirements

Citation: Leible S, Schlager S, Schubotz M and Gipp B (2019) A Review on Blockchain Technology and Blockchain Projects Fostering Open Science. Front. Blockchain 2:16. doi: 10.3389/fbloc.2019.00016

Received: 22 July 2019; Accepted: 08 October 2019; Published: 19 November 2019.

Reviewed by:

Copyright © 2019 Leible, Schlager, Schubotz and Gipp. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stephan Leible, stephan.leible@hs-offenburg.de

This article is part of the Research Topic

Blockchain for Distributed Research

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Research Article

Where Is Current Research on Blockchain Technology?—A Systematic Review

Affiliation Dept. of Innovation and Software, Lappeenranta University of Technology, Lappeenranta, Finland

Affiliation Dept. of Computer Science & Engineering, Sogang University, Seoul, South Korea

* E-mail: [email protected]

Affiliation Sogang Institute of Advanced Technology, Sogang University, Seoul, South Korea

Affiliation Dept. of Computer Science, Aalto University, Helsinki, Finland

Jesse Yli-Huumo,
Deokyoon Ko,
Sujin Choi,
Sooyong Park,
Kari Smolander

Published: October 3, 2016
https://doi.org/10.1371/journal.pone.0163477
Reader Comments

Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.

Citation: Yli-Huumo J, Ko D, Choi S, Park S, Smolander K (2016) Where Is Current Research on Blockchain Technology?—A Systematic Review. PLoS ONE 11(10): e0163477. https://doi.org/10.1371/journal.pone.0163477

Editor: Houbing Song, West Virginia University, UNITED STATES

Received: May 10, 2016; Accepted: September 9, 2016; Published: October 3, 2016

Copyright: © 2016 Yli-Huumo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Currency transactions between persons or companies are often centralized and controlled by a third party organization. Making a digital payment or currency transfer requires a bank or credit card provider as a middleman to complete the transaction. In addition, a transaction causes a fee from a bank or a credit card company. The same process applies also in several other domains, such as games, music, software etc. The transaction system is typically centralized, and all data and information are controlled and managed by a third party organization, rather than the two principal entities involved in the transaction. Blockchain technology has been developed to solve this issue. The goal of Blockchain technology is to create a decentralized environment where no third party is in control of the transactions and data.

Blockchain is a distributed database solution that maintains a continuously growing list of data records that are confirmed by the nodes participating in it. The data is recorded in a public ledger, including information of every transaction ever completed. Blockchain is a decentralized solution which does not require any third party organization in the middle. The information about every transaction ever completed in Blockchain is shared and available to all nodes. This attribute makes the system more transparent than centralized transactions involving a third party. In addition, the nodes in Blockchain are all anonymous, which makes it more secure for other nodes to confirm the transactions. Bitcoin was the first application that introduced Blockchain technology. Bitcoin created a decentralized environment for cryptocurrency, where the participants can buy and exchange goods with digital money.

However, even though Blockchain seems to be a suitable solution for conducting transactions by using cryptocurrencies, it has still some technical challenges and limitations that need to be studied and addressed. High integrity of transactions and security, as well as privacy of nodes are needed to prevent attacks and attempts to disturb transactions in Blockchain [ 1 ]. In addition, confirming transactions in the Blockchain requires a computational power.

It is important to identify what topics have been already studied and addressed in Blockchain and what are currently the biggest challenges and limitations that need further studies. To address these questions, we decided to use a systematic mapping study process [ 2 ] to identify relevant papers related to Blockchain. In the systematic mapping study, we applied a well-designed research protocol to search for material in scientific databases. The produced map of current research on Blockchain will help other researchers and practitioners in identifying possible research areas and questions for future research.

Although cryptocurrencies are also a business and management topic, we decided to narrow down the research topic to the technical perspective of Blockchain. Our objective was to find and map all papers with technical viewpoints on Blockchain. We were interested in finding Blockchain research topics related to various technical areas, such as security, performance, data integrity, privacy, and scalability.

The rest of the paper is organized as follows. Section 2 introduces the background of Blockchain and Bitcoin. In addition, we present some already identified challenges and technical limitations of Blockchain technology. In Section 3, we describe the applied research methodology and the process of collecting relevant research papers. Section 4 presents the results of the gathered papers and extracted data. Section 5 presents the identified classification schemes. Section 6 discusses the study and answers the research questions. Section 7 concludes the paper.

Blockchain, mostly known as the technology running the Bitcoin cryptocurrency, is a public ledger system maintaining the integrity of transaction data [ 1 ]. Blockchain technology was first used when the Bitcoin cryptocurrency was introduced. To this day, Bitcoin is still the most commonly used application using Blockchain technology [ 3 ]. Bitcoin is a decentralized digital currency payment system that consists of a public transaction ledger called Blockchain [ 4 ]. The essential feature of Bitcoin is the maintainability of the value of the currency without any organization or governmental administration in control. The number of transfers and users in the Bitcoin network is constantly increasing [ 5 ]. In addition, the conversions with traditional currencies, e.g. KRW, EUR and USD, occur constantly in currency exchange markets [ 6 ][ 7 ]. Bitcoin has therefore gained the attention of various communities and is currently the most successful digital currency using Blockchain technology [ 6 ].

Bitcoin uses the public key infrastructure (PKI) mechanism [ 8 ]. In PKI, the user has one pair of public and private keys. The public key is used in the address of the user Bitcoin wallet, and the private key is for the authentication of the user. The transaction of Bitcoin consists of the public key of the sender, multiple public keys of the receiver, and the value transferred. In about ten minutes, the transaction will be written in a block. This new block is then linked to a previously written block. All blocks, including information about every transaction made, are stored in the disk storage of the users, called nodes. All the nodes store information about all recorded transactions of the Bitcoin network and check the correctness of each new transaction made by using previous blocks. The nodes are rewarded by checking the correctness of transactions. This method is called mining, and it is confirmed with Proof-of-Work, which is one of the main concepts of Blockchain technology. When all transactions are successfully confirmed, a consensus exists between all the nodes. The new blocks are linked to previous blocks and all the blocks are aligned in one continuous chain. This chain of blocks is the public ledger technique of Bitcoin, called Blockchain.

Blockchain is the decentralized managing technique of Bitcoin, designed for issuing and transferring money for the users of the Bitcoin currency. This technique can support the public ledger of all Bitcoin transactions that have ever been executed, without any control of a third party organization [ 1 ]. The advantage of Blockchain is that the public ledger cannot be modified or deleted after the data has been approved by all nodes. This is why Blockchain is well-known of its data integrity and security characteristics. Blockchain technology can also be applied to other types of uses. It can for example create an environment for digital contracts and peer-to-peer data sharing in a cloud service [ 1 ]. The strong point of Blockchain technique, data integrity, is the reason why its use extends also to other services and applications.

Blockchain technology has also some technical challenges and limitations that have been identified. Swan [ 1 ] presents seven technical challenges and limitations for the adaptation of Blockchain technology in the future:

Throughput : The potential throughput of issues in the Bitcoin network is currently maximized to 7tps (transactions per second). Other transaction processing networks are VISA (2,000tps) and Twitter (5,000tps). When the frequency of transactions in Blockchain increases to similar levels, the throughput of the Blockchain network needs to be improved.
Latency : To create sufficient security for a Bitcoin transaction block, it takes currently roughly 10 minutes to complete one transaction. To achieve efficiency in security, more time has to be spent on a block, because it has to outweigh the cost of double spending attacks. Double-spending is the result of successful spending of money more than once [ 9 ]. Bitcoin protects against double spending by verifying each transaction added to the block chain, to ensure that the inputs for the transaction have not been spent previously [ 9 ]. This makes latency a big issue in Blockchain currently. Making a block and confirming the transaction should happen in seconds, while maintaining security. To complete a transaction e.g. in VISA takes only a few seconds, which is a huge advantage compared to Blockchain.
Size and bandwidth : At the moment, the size of a Blockchain in the Bitcoin network is over 50,000MB (February 2016). When the throughput increases to the levels of VISA, Blockchain could grow 214PB in each year. The Bitcoin community assumes that the size of one block is 1MB, and a block is created every ten minutes [ 10 ]. Therefore, there is a limitation in the number of transactions that can be handled (on average 500 transaction in one block) [ 11 ]. If the Blockchain needs to control more transactions, the size and bandwidth issues have to be solved.
Security : The current Blockchain has a possibility of a 51% attack. In a 51% attack a single entity would have full control of the majority of the network’s mining hash-rate and would be able to manipulate Blockchain. To overcome this issue, more research on security is necessary.
Wasted resources : Mining Bitcoin wastes huge amounts of energy ($15million/day). The waste in Bitcoin is caused by the Proof-of-Work effort. There are some alternatives in industry fields, such as proof-of-stake. With Proof-of-Work, the probability of mining a block depends on the work done by the miner [ 12 ]. However, in Proof-of-Stake, the resource that is compared is the amount of Bitcoin a miner holds [ 12 ]. For example, someone holding 1% of the Bitcoin can mine 1% of the “Proof-of-Stake blocks” [ 12 ]. The issue with wasted resources needs to be solved to have more efficient mining in Blockchain.
Usability : The Bitcoin API for developing services is difficult to use. There is a need to develop a more developer-friendly API for Blockchain. This could resemble REST APIs.
Versioning, hard forks, multiple chains : A small chain that consists of a small number of nodes has a higher possibility of a 51% attack. Another issue emerges when chains are split for administrative or versioning purposes.

Overall, Blockchain as a technology has the potential to change the way how transactions are conducted in everyday life. In addition, the applications of Blockchain are not limited to cryptocurrencies, but the technology could be possibly applied in various environments where some forms of transactions are done. The research on the possibilities of Blockchain in applications is certainly an interesting area for future research, but at the moment Blockchain suffers from technical limitations and challenges. Anonymity, data integrity and security attributes set a lot of interesting challenges and questions that need to be solved and assessed with high quality research. Scalability is also an issue that needs to be solved for future needs. Therefore, to identify and understand the current status of research conducted on Blockchain, it is important to gather all relevant research. It is then possible to evaluate what challenges and questions have been tackled and answered, and what are the most problematic issues in Blockchain at the moment.

Research methodology

Systematic mapping study was selected as the research methodology for this study. The goal of a systematic mapping study is to provide an overview of a research area, to establish if research evidence exists, and quantify the amount of evidence [ 2 ]. In this study we follow the systematic mapping process described by Petersen et al. [ 13 ]. We also use guidelines for a systematic literature review described by Kitchenham and Charters [ 2 ] to search for relevant papers. We chose the systematic mapping process as our research methodology because our goal was to explore the existing studies related to Blockchain technology. The results of the mapping study would help us to identify and map research areas related to Blockchain technology and possible research gaps. The process for the systematic mapping study is presented in Fig 1 , and consists of five process steps and outcomes. The Prisma Checklist is provided in S1 Checklist .

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https://doi.org/10.1371/journal.pone.0163477.g001

Definition of research questions

The first stage of the systematic mapping process is the definition of the research questions. The goal of this study was to provide an overview of the current research on Blockchain technology. Therefore, we defined four research questions:

RQ1: What research topics have been addressed in current research on Blockchain? The main research question of this mapping study is to understand the current research topics on Blockchain. By collecting all the relevant papers from scientific databases, we would be able to create an overall understanding of Blockchain research and map the current research areas. Mapping the current research done on Blockchain technology will help other researchers and practitioners to gain better understanding on the current research topics, which will help to take the research on Blockchain even further.
RQ2: What applications have been developed with and for Blockchain technology? Blockchain is mostly known for its relation to Bitcoin cryptocurrency. Bitcoin uses Blockchain technology in currency transactions. However, Bitcoin cryptocurrency is not the only solution that uses Blockchain technology. Therefore, it is important to find the current applications developed by using Blockchain technology. Identifying other applications can help to understand other directions and ways to use Blockchain.
RQ3: What are the current research gaps in Blockchain research? A systematic mapping of research enables understanding the current research gaps. The identification of research gaps will help other researchers and practitioners to focus their research on areas that require more research. Finding research gaps will help to understand and find unanswered research questions in current Blockchain technology.
RQ4: What are the future research directions for Blockchain? Understanding the potential future research directions for Blockchain technology is a consequence of RQ1-RQ3. Answering this research question is beneficial when deciding where the research on Blockchain technology should be directed and what issues need to be solved.

Conducting the search

The second stage of a mapping study is to search for all the relevant scientific papers on the research topic. A search protocol defines the methods that will be used to undertake a specific systematic literature search. A pre-defined protocol is needed to reduce the possibility of researcher bias [ 2 ].

We created a search protocol that we used for scientific databases to gather all the papers relevant for our research topic. The terms used in the search string were chosen after pilot searches, where we tested possible keywords. After the pilot search we decided to use only the term Blockchain as the search string, even though Bitcoin could also have been a possible one. However, in the pilot search we used also Bitcoin as a search term, but we identified a huge number of papers that were related to economic topics in cryptocurrencies, rather than technological aspects of Blockchain technology. Therefore, since our goal in this mapping study process was to find and map the papers related to technical aspects of Blockchain technology, we decided to drop the term Bitcoin. We believe that by using only the term Blockchain as the search string, the majority of Bitcoin-related papers with a technical perspective on Blockchain were still included. In addition, it seemed that if a Bitcoin-related paper did not have the term Blockchain anywhere in its meta-data, the paper was related to the economics of a cryptocurrency.

After designing and testing the search protocol, we chose the scientific databases for the searches. We decided to concentrate on peer-reviewed, high quality papers published in conferences, workshops, symposiums, books and journals related to the research topic. We used six scientific databases for paper retrieval. The chosen databases were (1) IEEE Xplore, (2) ACM Digital Library, (3) Springer Link, (4) ScienceDirect, (5) Ebsco, and (6) PLOS One. We decided not to use grey literature e.g. from Google searches, and kept scientific peer review as the criterion.

Screening of relevant papers

Because all papers in the searchers were not necessarily related to the research questions, they needed to be assessed for their actual relevance [ 2 ]. After using the search protocol in the scientific databases, the next stage was the screening of papers. For screening the relevant papers, we used a process inspired by Dybåand Dingsøyr [ 14 ]. At the first screening phase, we screened the papers based on their titles and excluded studies that were not relevant to the research topic. For example, the search protocol returned papers related to Blockchain in other scientific fields, which had different meaning than the Blockchain technology used in computer science. These papers were clearly out of the scope of this mapping study, which was a valid reason to exclude them. However, in some cases it was difficult to determine the relevancy of the paper on the basis of the title of the paper. In these situations, we passed the paper through to the next stage for further reading. In the second phase, the authors read the abstracts of every paper that passed the previous phase. In addition, we used specific inclusion and exclusion criteria to screen each paper. We decided to exclude the following types of papers: (1) papers without full text availability, (2) papers where the main language was not English, (3) papers that had some other meaning than Blockchain used in computer science, (4) papers that were duplicates, and (5) papers that were posters. When a paper passed all the five exclusion criteria, and after reading the abstract it was considered as focusing on Blockchain, we decided to include it in the next screening stage.

Keywording on the basis of the abstract

The next stage in a mapping study process after finding the relevant papers through abstracts is keywording. For this stage, we used the process defined by Petersen et al. [ 13 ] ( Fig 2 ). Keywording was done in two steps. In the first step we read the abstract and identified keywords and concepts that reflected the contribution of the paper [ 13 ]. The second step was to develop a higher level of understanding based on these keywords [ 13 ]. We used the keywords to cluster and form categories for the mapping of the studies. After the categories had been clustered, we read all the selected papers. After the reading we also updated the categories or created new ones, if the paper revealed something new. This resulted in a systematic map of clustered categories formed from all the relevant papers on the research topic.

https://doi.org/10.1371/journal.pone.0163477.g002

Data extraction and mapping process

A data extraction form ( Table 1 ) was designed to collect the information needed to address the research questions of this mapping study [ 2 ]. Data items DI0 to DI6 gathered basic information of the papers. These items included e.g. the title of the paper, the name(s) of the author(s), the country of the author(s), and publication type/place. The rest of the data items (DI7-DI10) were gathered after reading the papers. These data items included e.g. study goals and major findings of each paper. We collected the extracted data items to Excel, which helped us to organize and analyze the data.

https://doi.org/10.1371/journal.pone.0163477.t001

Basic information of the papers

In this section, the search and selection results of the systematic mapping study are presented. Out of the extracted data items ( Table 1 ), this section reports on data items DI0-DI6.

Search and selection results

The search and selection results are presented in Fig 3 . The PRISMA flow diagram is also provided in S1 Diagram . 121 papers were initially retrieved when the designed search protocol was applied to the selected scientific databases. The first inclusion and exclusion round was based on the titles of the retrieved papers. All the paper titles were examined by two authors, which led to the selection of 55 papers. The reason for the high number of excluded papers (66) was that they were not related to the research topic. For example, many excluded papers discussed the business perspective of Bitcoin, and therefore they did not belong to our study. We also retrieved multiple papers related to other scientific areas, such as chemistry and mathematics, where the keyword Blockchain had another meaning than the technology used in computer science.

https://doi.org/10.1371/journal.pone.0163477.g003

After the selection of 55 papers, we removed duplicates and used the next round exclusion and inclusion criteria defined in section 3.3. This round resulted in the selection of 48 papers. After this, three authors read the abstracts of all the selected papers. This did not result in the exclusion of any papers, however. Based on the abstracts, all the selected papers had a topic related to Blockchain with a technical viewpoint.

However, we decided to pass some unclear papers to the next selection round for more in-depth analysis. In the last stage of paper selection, three authors read all the papers. This resulted in the selection of 41 papers, which we included in this study as primary papers. Three papers were dropped due to their focus on the economic perspective of Blockchain and Bitcoin. Additional four papers were excluded for being only reports describing Blockchain and how it works without providing any actual new research findings or evidence. The full list of the selected papers with the extracted data items is presented in S1 Table .

Publication year, source and geographic distribution

Fig 4 shows the publication year distribution of the selected primary papers. Interestingly, all the selected papers were published after the year 2012. This shows that Blockchain as a research area is a very recent and new one. When looking at the publication year distribution more closely, out of all the selected papers, 2 papers (5%) were published in 2013, 16 papers (39%) in 2014 and 23 papers (56%) in 2015. This shows an increasing number of publications each year, which suggests also a growing interest in Blockchain technology. This is not a surprise, because the idea of Blockchain and Bitcoin was first coined only in 2008 [ 4 ].

https://doi.org/10.1371/journal.pone.0163477.g004

Fig 5 shows the source of each selected primary paper. The possible sources for a paper are the academia, industry, or both. Our results showed that 30 papers (73.1%) were published by an academic source and only 3 papers (7.3%) were published by an industry source. In 8 papers (19.5%), the authors were from both academia and industry. It is, however, highly possible that most of the papers published by the industry are not included in scientific databases. Most industry papers can be found as white papers and are not often published in peer-reviewed conferences or journals.

https://doi.org/10.1371/journal.pone.0163477.g005

The geographical distribution of the selected papers is shown in Fig 6 . The largest number of papers (13, 31%) were published by universities or companies in the USA. After this, the two most common publication countries were Germany with 6 papers (14.6%) and Switzerland with 5 papers (12.2%). The rest of the countries had four or less papers published. The geographical distribution of the selected primary papers shows that Blockchain technology has gathered research interest around the world.

https://doi.org/10.1371/journal.pone.0163477.g006

Publication type and channel

Fig 7 shows the publication type of the selected papers. Publication type means the channel where the paper has been published. The publication types included in this mapping study were conference, journal, workshop, symposium, and book chapter. Most of the papers were published in conferences (23) (56%) and workshops (12) (29.2%). The rest of the papers were published in symposiums (4) (9.7%), as a book chapter (1) (2.4%), or in a journal (1) (2.4%). In addition, Table 2 shows the publication channel of each selected paper. Most papers were published in conferences and workshops in the International Conference on Financial Cryptography and Data Security (FC) (13) (31.7%). 3 or less of the selected papers had used other publication channels.

https://doi.org/10.1371/journal.pone.0163477.g007

https://doi.org/10.1371/journal.pone.0163477.t002

Classification of the relevant papers

In this section, the classification of the selected primary papers is presented, including extracted data items DI7-DI10 ( Table 1 ). After reading all the selected papers and creating classifications based on the findings, we identified that a majority of the papers were related to the technical challenges and limitations presented by Swan [ 1 ]. Therefore, we decided to use these challenges and limitations for the classification to map the existing research on Blockchain. The challenges and limitations presented by Swan are throughput, latency, size and bandwidth, security, wasted resources, usability, versioning, hard forks, and multiple chains. In addition, we identified a new classification type, privacy. Privacy in an essential attribute in the Blockchain environment, because of its anonymity characteristic. In addition, we also used the class others to map papers that were not related to any of the classes mentioned above.

We also identified that there were three different paper types for each class, Blockchain report, Blockchain improvement and Blockchain application. A Blockchain report includes papers that report previously identified solutions and ideas in Blockchain and Bitcoin. A Blockchain improvement includes papers that suggest new solutions and improvements to the current Blockchain or Bitcoin technology. A Blockchain application includes papers that present an application based on Blockchain technology. The final map of this study is presented in Fig 8 .

https://doi.org/10.1371/journal.pone.0163477.g008

We also decided to examine the papers based on their relation to Bitcoin ( Fig 9 ), because it is considered so far the most important and commonly used solution based on Blockchain technology. As expected, a great number of papers were related to Bitcoin, rather than other applications. In 33 (80.5%) of the selected papers, the research was conducted in the Bitcoin environment. We found only 8 papers (19.5%) that did focus on Bitcoin, but on other applications using Blockchain technology.

https://doi.org/10.1371/journal.pone.0163477.g009

We also made a comparison between the paper type (Blockchain report, Blockchain improvement, and Blockchain application) and the publication year. The comparison is shown in Fig 10 . The figure shows an increasing number of papers in both report and application categories over the three years. Improvement papers had a significant increase in 2014, but a decrease in 2015.

https://doi.org/10.1371/journal.pone.0163477.g010

Security was the one of the major research topics in the selected primary papers. 14 out of the 41 papers (34%) were related to challenges and limitations in Blockchain and Bitcoin security. We identified various topics in security, including trends and impacts of security incidents, 51% attack, data malleability problems, and authentication and cryptography issues.

Trends and impacts of security incidents : With the increasing use of Bitcoin as a way to conduct payments and transfers, security incidents and their impact on the economic losses of Bitcoin users have increased. Some of the identified papers presented security incidents that had occurred in the Bitcoin network, such as economic losses by several Bitcoin scams and distributed denial-of-service (DDoS) attacks on exchanges and mining pools. Vasek et al. [ 33 ] investigated four types of Bitcoin scams (Ponzi scams, mining scams, scam wallet and fraudulent exchanges) by tracking online forums and voluntary vigilantes. The authors noted that $11 million had been contributed to scams by 13000 victims in Bitcoin from September 2013 to September 2014. Lim et al. [ 48 ] analyzed the trend of security breaches in Bitcoin and their countermeasures. According to the authors, all possible types of security breaches had occurred, including DDoS attacks, private account hacking using Trojan horses, or viruses from ads. The authors introduce some security countermeasures for individual users and safe Bitcoin transactions (e.g. a hardware wallet and a hardware authentication device). Vasek et al. [ 27 ] present evidence on DDoS attacks in the Bitcoin network using DDoS-related posts in the popular Bitcointalk.org forum. The authors figured out that the most targeted service category was the use of anti-DDoS protection, influencing factors such as the mining pool size. The major findings of the study were that the most often targeted service was currency exchange (41%), followed by mining pools (38%). According to the paper, 54% of the services that had experienced DDoS attacks had anti-DDoS protection, although it was not certain whether they had the protection on at the time of attack. In addition, of the services that had not yet experienced a DDoS attack, only 15% had anti-DDoS protection. The paper concludes that over 60% of large mining pools have suffered DDoS attacks, compared to 17% of small pools.

51% Attack : The Blockchain mechanism is designed with the assumption that honest nodes control the network [ 4 ]. If attacker nodes collectively control more computational power than the good ones, the network is vulnerable to the so called 51% Attack. Beikverdi et al. [ 20 ] argue that although the Bitcoin itself is designed as a fully decentralized network, market-based centralization of mining power by a few large mining pools increase the risk of a 51% Attack. Their study shows that the centralization factor of Bitcoin has been continuously increasing from 2011 (0.26) to 2014 (0.33). In this context, 0 means purely decentralized and 1 means fully centralized. Moreover, there are studies claiming that the 1/2 assumption of computational power is not enough for security. Garay et al. [ 39 ] propose applications built on the core of the Bitcoin protocol focusing on the Byzantine agreement (BA), which is the fundamental scientific problem for decentralized transaction agreement in the Bitcoin network. The suggested application presents a simple BA protocol with the assumption that the adversary’s hashing power is bounded by 1/3. Eyal and Sier [ 30 ] introduce a Selfish Mine attack where colluding miners obtain a revenue larger than a fair share by keeping their discovered blocks private. The authors propose a protocol modification which commands less than 1/4 of the total computation power.

The more recent Blockchain-based systems, such as Ethereum, allow users to specify scripts in transactions and contracts to support applications beyond simple cash transactions. In this case, the required computational resources for verification could be larger, depending on the user-specified script size. Luu et al. [ 46 ] present a security attack called the verifier’s dilemma, which drives rational miners to skip verification where the verifying transactions require significant computational resources in Bitcoin and especially in Ethereum. The authors formalize a consensus model to give incentives to miners by limiting the amount of work required to verify a block.

Armknecht et al.[ 42 ] explain how to support security and privacy in the Ripple system, which is one of the consensus-based distributed payment protocols. The paper discusses the basic difference between the protocol of Ripple and Bitcoin-focused Blockchain fork. A fork can occur if two conflicting ledgers get a clear majority of votes, and could lead to double spending attacks. According to Decker and Wattenhofer [ 52 ], the propagation delay in the Bitcoin network is the primary cause for Blockchain forks and inconsistencies among replicas, which was done by analyzing Blockchain synchronization mechanism.

Data malleability problems : Data integrity is an essential issue in the Blockchain environment. It is necessary that when data gets sent and verified, it has not been altered or tampered with. We found two studies related to data integrity that studied malleability attacks in Blockchain. Malleability describes the fact that the signatures that prove the ownership of Bitcoin being transferred in a transaction do not provide any integrity guarantee for the signatures themselves [ 36 ]. Therefore, in a malleability attack an attacker intercepts, modifies, and rebroadcasts a transaction, causing the transaction issuer to believe that the original transaction was not confirmed [ 36 ].

Decker & Wattenhoffer [ 36 ] studied transaction malleability in Bitcoin environment and used a real-life case as an example. According to the paper, the transaction malleability problem is real and should be considered when implementing Bitcoin clients. Andrychowicz et al. [ 31 ] made a similar study by conducting practical experiments which presented a high possibility of a malleability attack and its impact. In their study, the malleability attack caused incorrect balance computing, application crashes, and a deadlock which stopped new transactions in several well-known Bitcoin wallets. The paper suggests a deposit protocol with a timed commitment scheme to enable a malleability-resilient refund transaction as a solution to the malleability problem.

Authentication and cryptography issues : In Bitcoin, the private key is the major authentication element. Authentication in cryptocurrency controls self-certification. There have been some incidents with authentication. For example, there is the well-known case in Mt.Gox, where a Bitcoin wallet company was attacked. In the attack, Mt.Goxs storage that included private keys of their customer was stolen. This incident has motivated some studies in strengthening authentication in Bitcoin. In addition to the Mt.Gox case, Bos et al. [ 26 ] state that the use of elliptic curve cryptography (ECC), which is used to derive Bitcoin addresses to users, is insufficient and does not have the required randomness.

We identified a number of papers that had the goal to address the issues in the Bitcoin authentication process. Bamert et al. [ 18 ] suggest a Bitcoin hardware token, the BlueWallet. The device communicates by using Bluetooth Low Energy, and is able to secure and sign Bitcoin transactions. Ateniese et al. [ 19 ] propose a certification system for Bitcoin that offers an opt-in guarantee to send and receive Bitcoins only to/ from certified users, and control of the creation of Bitcoins addresses by trusted authorities. According to the paper, this approach improves the trustworthiness of real-world entities into the system, which mitigates the existing reservations to the adoption of Bitcoin as a legitimate currency. Mann et al. [ 17 ] suggest two-factor authentication for a Bitcoin wallet. The authors used a smart phone as the second authentication factor. The solution can be used with hardware already available to most users, and the user experience/interface has similarities to the existing online banking authentication methods.

Wasted resources

The energy efficiency problem is not handled in the computer engineering field at the moment. However, in special domains like mobile cloud computing, it might be one of the major issues in the future [ 55 ]. Mining Bitcoins requires a high amount of energy to compute and verify transactions securely and with trustworthiness [ 1 ]. However, for the efficiency of mining and Proof-of-Work, it is important to decrease the amount of wasted resources.

We identified some papers related to the wasted resource problems in Bitcoin. Wang and Liu [ 21 ] present the evolution of Bitcoin miners in terms of volume of solo and pool miners and their productivity. In the early stages, the computation power was evenly distributed among the solo miners. As the Bitcoin network evolved, the computation power of some pool miners increased. The study notes that all miners play a zero-sum-computation race game: each miner increases their computation power, and then the total computation power in the network increases; consequently the system increases the difficulty value to maintain a steady Bitcoin creation speed, which in turn reduces the Bitcoin mining rate of individual miners [ 21 ].

We also identified some papers that proposed solutions for the wasted resources problem in Blockchain and Bitcoin. Wang and Liu [ 21 ] suggest an economic model for getting high economic returns in consideration of the use of mining hardware with high computation-over-power efficiency and electricity price. Paul et al. [ 16 ] have calculated and show how a new scheme can lead to an energy-efficient Bitcoin. The authors modified the present block header by introducing some extra bytes to utilize the timestamp more effectively. The suggested scheme uses less computing power, and thus the mining is more environment-friendly. Anish [ 15 ] proposes methods of achieving contextually higher speeds of Bitcoin mining, involving simultaneous usage of CPUs and GPUs in individual machines in mining pools. The results presented in the paper show how standard hardware miners in large mining pools could quite significantly add to the overall hash rate. Barkatullah et al. [ 54 ] describe the architecture and implementation details of a CoinTerras first-generation Bitcoin mining processor, Goldstrike 1, and how this processor was used to design a Bitcoin mining machine called Terraminer IV, especially about how high power density issues were solved and energy efficiency increased.

The original definition of the challenges and limitations in the usability of Blockchain by Swan [ 1 ] describes Bitcoin API as hard and difficult to use. This definition can be viewed mainly from the developer’s perspective, where Bitcoin API is hard to implement and use in and with other services and applications. We did not find any papers related to the usability issue from the software developer’s perspective. However, we found several papers that considered the usability of Bitcoin from the cryptocurrency user’s perspective. Therefore, we decided to expand the original definition of Blockchain usability to take usability into account also from the point of view of the cryptocurrency user.

An important factor in Blockchain usability from the user’s perspective is the ability to analyze Blockchain. In Blockchain, new blocks are created constantly and confirmed by miners, which creates an interesting environment of transaction flows. It is therefore essential to have supporting tools to help users analyze the whole Blockchain network to improve the usability. We found applications that had been developed for this purpose. BitConeView [ 51 ] is a system for the visual analysis of Bitcoin flows in Blockchain. BitIodine [ 23 ] parses Blockchain, clusters addresses that are likely to belong to the same user or group of users, classifies such users and labels them, and finally visualizes the complex information extracted from the Bitcoin network. Both these systems were tested successfully with experiments and cases, and showed effectiveness in analyzing and detecting patterns in the Bitcoin network. These systems can help also in improving security and privacy -related issues.

Bankruptcy and the closure of Bitcoin exchanges can cause economical damage to the customers [ 38 ]. Decker et al. [ 38 ] propose an audit software to improve usability in Bitcoin exchanges. The goal of the software is to prove the exchange participants’ solvency without publishing important information. In addition, Vandervort [ 25 ] discusses the link between a buyer and a seller with a layer of limited anonymity, thus preventing buyers from finding or validating information in Bitcoin. The paper presents three different models by which a reputation/rating system could be implemented in conjunction with Bitcoin transactions, and considers the pros and cons of each. Improving these aspects of exchanges done in the Bitcoin network can improve the usability by providing additional information for the users making the transactions.

Throughput, latency, size and bandwidth, and versioning, hard forks, multiple chains

Interestingly, we did not identify any papers that were related to other technical challenges and limitations, such as throughput, latency, size and bandwidth, versioning, hard forks, and multiple chains.

In a Blockchain network, a distributed consensus network without a trusted party, all the transactions are transparent and announced to the public. Therefore, privacy in Blockchain is maintained by breaking the flow of information. The public can see all transactions, but without information linking the transaction to identities [ 4 ]. For this security model, 10 studies out of 41 (24%) proposed privacy issues and countermeasures to increase anonymity in Blockchain.

Meiklejohn and Orlandi [ 32 ] present a definitional framework of anonymity focusing on the ownership of the coin. There are also studies that show experimental evidence on the lack of anonymity in the Bitcoin network. Koshy et al. [ 35 ] analyzed a traffic pattern in Bitcoin and conclude that some subset of Bitcoin addresses can be mapped to an IP address simply by observing the transaction relay traffic. Feld et al. [ 53 ] introduce a framework to traverse the Bitcoin network and generate statistics based on that. By using the tool, the authors figured out that an average peer-list contains addresses that mostly reside in the own autonomous systems of the peers. Taking this information into account, the authors claim that transaction linking could be possible.

Similar to our mapping study, Herrera-Joancomartí [ 6 ] provide an exhaustive review of papers on Bitcoin anonymity research. according to the author, very few papers have been published regarding the traffic of Bitcoin that may reveal private information. In order to solve the anonymity reduction, a mix of services has been proposed in some papers. A number of studies have applied a transaction mixing technique to increase privacy. A mixing transaction allows the users to move Bitcoins from one user address to another without a clear trace linking between the addresses. Such transactions can act as a primitive to help improve anonymity when transaction linking becomes more challenging.

Valenta and Rowan [ 24 ] have modified the Mixcoin protocol to prevent the mix from learning the input/output address mappings of participating users. The authors propose a system, Blindcoin, which modifies the Mixcoin mixing protocol by using blind signatures and a public append-only log. The log makes it possible for a third party to verify the validity of accusations when blind signatures are used. Ziegeldorf et al. [ 47 ] present CoinParty, a decentralized mixing service for Bitcoin based on a combination of decryption mix-nets with threshold signatures. According to the authors, CoinParty is secure against malicious adversaries, and the evaluation of their prototype shows that it scales easily to a great number of participants in real-world network settings. Ruffing et al. [ 37 ] propose CoinShuffle, a completely decentralized Bitcoin mixing protocol that allows the users to utilize Bitcoin in a truly anonymous manner. It does not require any (trusted, accountable or untrusted) third party and it is compatible with the current Bitcoin system. CoinShuffle introduces only a small communication overhead for its users, while avoiding additional anonymization fees and minimizing the computation and communication overhead for the rest of the Bitcoin system. Androulaki et al. [ 41 ] propose a solution, an extension of ZeroCoin (EZC), to hide transaction value and address balances in Bitcoin for increased privacy. ZeroCoin acts as a temporary currency to impede the traceability of coins, but it does not hide the number of transactions and balances of Bitcoin addresses. The proposed improvements include mixing Bitcoins from various sources before sending them to a destination and enabling payments in the form of EZC without the need to transform them back to Bitcoin. For the effectiveness of mixing techniques in improving anonymity, Möser et al. [ 49 ] present analysis results on some available Bitcoin mixing services. The test results showed that linking the input and output transactions was possible in 1 out of 3 tested services. Other than mixing techniques, Saxena et al. [ 28 ] suggest use of composite signatures to prevent linking between sending and receiving addresses.

Smart contracts, new cryptocurrencies, botnet, broadcast protocol, trustworthiness

We also identified other classifications that were not included in the seven technical challenges and limitations defined by Swan [ 1 ]. Three of the papers were related to the use of Smart contracts in the Blockchain environment. A smart contract is a solution that utilizes Blockchain technology to create contracts between two or more participants. Similarly to the use of Bitcoin Blockchain, smart contracts are done in a decentralized environment, where contract terms are executed by the Blockchain systemwhen the terms are fulfilled. Bigi et al. [ 45 ] introduce a decentralized smart contract protocol inspired by BITHALO and validated the feasibility of the protocol based on the protocols of Bitcoin. The approach is a combination of the game theory and formal models. The authors argue that a decentralized smart contract system can be a promising approach and worthy of being studied and developed further. Wan et al. [ 43 ] propose an electronic signing protocol between two parties using the Bitcoin network as a way of providing a time-stamping service. In addition, smart contracts can be possibly used in various environments and industries for different purposes. For example, Kishigami et al. [ 50 ] provide a Blockchain-based digital content distribution system and show a prototype of the concept. The idea was presented to one hundred people including creators, content owners and digital content stake holders. The feedback showed that the most impressive point was the decentralized mechanism for Digital Right Management. However, the proposed system has no incentive mechanism for mining calculation, which can make it a challenge to adopt at the moment.

Even though Bitcoin is the most famous and commonly used cryptocurrency adopting Blockchain technology, there has also been research on developing other cryptocurrencies. Zhang and Wen [ 22 ] have designed a new generation cryptocoin called IoTcoin, based on the protocol of Bitcoin and Blockchain. In IoT-coin, people can use keys and scripts which are obtained in them to exchange paid sensor data or smart property. IoTcoins can be used to present the ownership of many IoT commodities, such as smart property, paid data and digital controlled energy. Another cryptocurrency has been proposed by Vandervort et al. [ 29 ] as a model of a community cryptocurrency with a community fund feature.

We also found three papers that used Blockchain for Botnet networks, a P2P broadcast protocol, and a trustworthiness improvement. Ali et al. [ 34 ] ppresent Zombiecoin, which runs in Bitcoin networks and offers a Botnet C&C (command-and-control) mechanism. Botnet networks include a number of computers communicating in an effort to compute representative tasks. However, the weak point for botnet is the C&C infrastructure. The Bitcoin transaction can be used as a communication vehicle. Andrychowicz and Dziembowsk [ 40 ] ppresent a formal model for peer-to-peer communication and a Proof-of-Work concept used in Bitcoin, and based on the model, propose a broadcast protocol which is more secure against an adversary with arbitrary computational power. Wilson and Ateniese [ 44 ] have adopted the Bitcoin technology to enhance the Pretty Good Privacy (PGP) mechanism. In this mechanism, a Bitcoin address, Bitcoin identity verification transactions, and a Blockchain key server are used to improve the user’s trustworthiness.

Summary of the identified challenges/limitations and suggested solutions in Blockchain

In Fig 11 we summarize the identified challenges and suggested solutions in Blockchain and Bitcoin.

https://doi.org/10.1371/journal.pone.0163477.g011

In this chapter we discuss the results and answer the four main research questions. In addition, at the end of this chapter, we discuss the limitations and validity of the study.

RQ1: What research topics have been addressed in current research on Blockchain?

The results of this mapping study showed that a majority of the current research on Blockchain is focused on finding and identifying improvements to the current challenges and limitations in Blockchain [ 1 ]. A large portion of the research concentrates on security and privacy issues in Blockchain.

The security vulnerability of the Blockchain network and the growing interest in Bitcoin have increased the economic losses of both miners and end users. The identified vulnerabilities include computation power -based attacks, such as the 51% attack, selfish mine attack, transaction data malleability problems, and deanonymization by transaction linking. Although several solutions to address these issues have been presented, many of them are just brief idea suggestions, lacking concrete evaluation of their effectiveness.

The research on other topics in challenges and limitations described by Swan [ 1 ], such as wasted resources and usability, was rather limited. We found some research done on computational power and wasted resources in Bitcoin mining, and improvements on the usability of Bitcoin. However, the number of papers was considerably small compared to those on security and privacy issues. Computational power is one of the key attributes in Blockchain, and it requires attention in the research. When Blockchain grows more complex, it also requires more computational power to confirm more blocks. The Proof-of-Work concept is a rather new idea, which is the reason why it has to be studied more, to make sure that it can work in large-scale Blockchain environments.

Interestingly, we did not find many studies on challenges and limitations in latency, size and bandwidth, throughput, versioning, hard forks, and multiple chains. It is surprising that the attention paid to and research done on other challenges and limitations than security and privacy was rather low. We assumed that especially topics like latency, size and bandwidth, and wasted resources would have received more attention in the overall research map. When the size of Blockchain increases, it has a direct impact on all these challenges and limitations in scalability. It is possible that these issues have not been studied a lot because the Blockchain concept is still rather new.

In addition to the identified research topics, the findings in this mapping study showed that a majority of research was conducted in the Bitcoin environment. This was also the original assumption of the authors, considering that Bitcoin is currently the most commonly used and important technology using Blockchain, with the largest user base. However, we were quite surprised that the number of other solutions than Bitco using Blockchain was so low. The results showed that the research outside the Bitcoin environment was mostly focused on smart contracts and other cryptocurrencies, but the research on Bitcoin and its security issues formed the majority.

RQ2: What applications have been developed with and/to Blockchain technology?

We originally defined a Blockchain application as a solution that has been developed with Blockchain technology. By this definition, we identified some prototype applications developed and suggested for using Blockchain in other environments, such as IoT, smart contracts, smart property, digital content distribution, Botnet, and P2P broadcast protocols. This shows that Blockchain technology is not limited to applications in cryptocurrencies. Instead, the idea of a public ledger and a decentralized environment can be applied to various other applications in different industries, which makes the whole Blockchain research more interesting.

However, we also found a set of different applications developed for the Bitcoin environment, rather than using Blockchain technology in some other environment. Some of the applications were developed for Bitcoin analysis. Applications like BitConeView [ 51 ] and BitIodine [ 23 ] help users to analyze the Bitcoin network and study how Bitcoin transactions are completed, with a visual presentation. These types of applications can help to understand the essence of Blockchain, and how a decentralized transaction environment actually works. Analysis applications can also help to identify frauds and possible security issues by following the flows of transactions.

Another major direction for applications is security. We found applications where the focus was on Bitcoin mixers. Bitcoin mixing applications, such as CoinParty [ 47 ] and CoinShuffle [ 37 ] can help the Bitcoin network to become more secure, by adding an extra layer of privacy for the users. These types of applications and solutions will likely increase in the future, considering that security and privacy are the main attributes in a decentralized transaction environment.

RQ3: What are the current research gaps in Blockchain research?

We were able to identify a few major research gaps. The first gap is that the research on topics such as latency, throughput, size and bandwidth, versioning, hard forks, and multiple forks does not exist in the current literature. This is a major research gap, which requires more research in the future. These topics are not possibly the most interesting topics for researchers at the moment, because the sizes of the current Blockchain applications are relatively small. Bitcoin is currently the largest solution with Blockchain. The number of transactions in Bitcoin is considerably smaller than e.g. in VISA. However, in the future, if Blockchain solutions are used by tens of millions of people and the number of transactions is multiplied drastically, more research on e.g. latency, size and bandwidth, and wasted resources needs to be conducted to ensure scalability.

The second research gap is the lack of research on usability. We identified only papers that discussed usability from the user perspective, not from the developer perspective, as suggested by Swan [ 1 ]. For instance, the difficulty of using Bitcoin API has not been tackled yet. This needs to be studied and improved in the future. This could spark more applications and solutions to the Bitcoin environment.

The third research gap is that the majority of current research is conducted in the Bitcoin environment, rather than in other Blockchain environments. Research on e.g. smart contracts needs to be carried out to increase knowledge outside cryptocurrencies. Even though Blockchain was first introduced in the cryptocurrency environment, the same idea can be used in various other environments. Therefore, it is necessary to conduct research on the possibilities of using Blockchain in other environments, because it can reveal and produce better models and possibilities for doing transactions in different industries.

The fourth research gap can be found in the low number of high quality publications in journal level publication channels. Currently most of the research is published in conferences, symposiums and workshops. There is a need for high quality journals where the focus is on Blockchain.

RQ4: What are the future research directions for Blockchain?

The future research directions for Blockchain are not clear, and it is interesting to see where it is heading. On the other hand, Bitcoin has received a lot of attention as a cryptocurrency, and more people are trading and buying Bitcoins every day. Therefore, it is highly possible that Bitcoin is important as one of the future research topics, and it will attract industry and academia to conduct more research from both business and technical perspectives.

Bitcoin is only one solution using Blockchain technology. There are also a lot of other cryptocurrencies at the moment, competing with Bitcoin to be the world’s primary cryptocurrency. We believe that future research will also include research conducted on other cryptocurrencies. However, at the moment it seems that Bitcoin has by far the largest market share, and it will be a challenge for other cryptocurrencies to compete with it.

However, we believe that future research will not only focus on Bitcoin and other cryptocurrencies, but on other possible applications using Blockchain as a solution. We already found some papers that studied the possibility of using smart contracts, licensing, IoT, and smart properties in the Blockchain environment. We believe that this type of research will have a lot of impact in the future, and can possibly be even more interesting than cryptocurrencies. To use a decentralized environment in e.g. sharing a virtual property could be a solution that revolutionizes the way companies can sell their products. Taking this in consideration, we strongly believe that when Blockchain technology gets adopted more by both industry and academia, it will generate a significant amount of new research.

When more Blockchain solutions are taken in use with larger numbers of users, it will also have an impact on the research done on technical limitations and challenges. In the future, increased sizes and user bases in various Blockchains will trigger the need to conduct more research on the challenges and limitations in topics related to scalability. In addition, the security and privacy of Blockchain will be always a topic for research, when new ways are invented to disturb and attack Blockchain. Although Blockchain is a rather new technology, there already exist profound studies in each problem domain including security and distributed system literature (for example, multi-level authentication technique [ 56 ], energy-efficient resource management for distributed systems [ 55 , 57 ], and etc.). A closer look and adoption of proven solutions would accelerate overcoming current challenges and limitations of Blockchain technology.

Limitations of the systematic mapping study

The principal limitations of a systematic mapping study are related to publication bias, selection bias, inaccuracy in data extraction, and misclassification [ 58 ].

Publication bias refers to the problem that positive results are more likely to be published than negative ones, since negative results take longer to be published or are cited in other publications to a lesser extent [ 2 ][ 58 ]. To address this issue, we used several well-known scientific databases in the search protocol to find as many papers as possible. This increased the number of papers we were able to find for this mapping study, which to some extent also increased the possibility to find papers with negative results. However, considering that Blockchain technology is rather a new topic in computer science industry and academia, it is possible that research has been conducted in the industry and published as white papers or internally within companies. Therefore, all research conducted on the technical aspects on Blockchain might not be included in this mapping study. However, by using only scientific databases as a source for finding relevant research, we were able to collect papers that were probably of a higher quality.

Selection bias refers to the distortion of statistical analysis owing to the criteria used to select the publications [ 58 ]. We addressed this issue by designing our search protocol carefully. We also conducted a pilot search with different keywords, to ensure that we included as many papers as possible in this mapping study. We defined rigorous inclusion and exclusion criteria, to ensure that all the selected papers were part of our research topic, and answered the research questions. However, there is one major limitation that needs to be addressed. Our search protocol included only the term Blockchain. There is a possibility that not all the research related to Blockchain was found due to our search protocol for paper retrieval. Much of the research related to Blockchain concerns economic, legal, or regulation aspects of Bitcoin and its possibilities as a cryptocurrency. Our goal was to study the technical aspects of Blockchain, rather than trying to understand how Bitcoin as a cryptocurrency can work in the real-world environment. Based on our pilot search, we believe that we were able to retrieve a majority of the relevant papers by using only Blockchain as the search term.

Inaccuracy in data extraction and misclassification refer to the possibility that information is extracted differently by different reviewers [ 58 ]. We addressed this issue by using three authors in the paper retrieval process. All three authors went through the abstracts of the selected papers, and gave their opinion on including or excluding the paper. In a situation where the opinions did not match, we had a discussion to address whether that specific paper should be included or excluded. In addition, the classifications of the papers were done in several face-to-face meetings, where the three authors discussed and created classifications and mappings to all the 41 selected primary papers.

Blockchain technology runs the Bitcoin cryptocurrency. It is a decentralized environment for transactions, where all the transactions are recorded to a public ledger, visible to everyone. The goal of Blockchain is to provide anonymity, security, privacy, and transparency to all its users. However, these attributes set up a lot of technical challenges and limitations that need to be addressed.

To understand where the current research on Blockchain technology positions itself, we decided to map all relevant research by using the systematic mapping study process [ 2 ]. The goal of this systematic mapping study was to examine the current status and research topics of Blockchain technology. We excluded the economic, law, business, and regulation perspectives, and included only the technical perspective. We extracted and analyzed 41 primary papers from scientific databases. We provide recommendations on future research directions of Blockchain technology based on the current research status as following:

Continue to identify more issues and propose solutions to overcome challenges and limitations of Blockchain technology. The interest on Blockchain technology has been drastically increased since 2013. The cumulative number of papers is increased from 2 in 2013 to 41 in 2015. Majority of the studies has been focused on addressing the challenges and limitations, but there still exist many issues without proper solutions.
Conduct more studies on scalability issues of Blockchain. Most of the current research on the Blockchain technology is focused on security and privacy issues. To be ready for pervasive use of Blockchain technology, scalability issues such as performance and latency have to be addressed.
Develop more Blockchain based applications beyond Bitcoin and other cryptocurrency systems. The current research is focused on Bitcoin system. However, the research also shows that Blockchain technology is applicable for other solutions such as smart contracts, property licensing, voting etc.
Evaluate the effectiveness of the proposed solutions with an objective evaluation criteria. Although several solutions to challenges and limitations have been presented, many of them are just brief idea suggestions and lack concrete evaluation on their effectiveness.

Supporting Information

S1 table. the full list of selected primary papers..

https://doi.org/10.1371/journal.pone.0163477.s001

S1 Checklist. PRISMA Checklist.

https://doi.org/10.1371/journal.pone.0163477.s002

S1 Diagram. PRISMA Flow diagram.

https://doi.org/10.1371/journal.pone.0163477.s003

Author Contributions

Conceptualization: JY DK SC SP KS.
Data curation: JY DK SC.
Formal analysis: JY DK SC.
Investigation: JY DK SC.
Methodology: JY DK SC.
Project administration: SC.
Resources: SP KS.
Supervision: SP KS.
Visualization: JY.
Writing – original draft: JY DK.
Writing – review & editing: JY DK SC.
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Blockchain-based privacy and security preserving in electronic health: a systematic review

Published: 17 February 2023
Volume 82 , pages 28493–28519, ( 2023 )

Cite this article

Kianoush Kiania 1 ,
Seyed Mahdi Jameii ORCID: orcid.org/0000-0002-9407-665X 2 &
Amir Masoud Rahmani 3

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In today’s world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed.

Blockchain for Security and Privacy of Healthcare Systems: A Protocol for Systematic Literature Review

A Systematic Review for Enabling of Develop a Blockchain Technology in Healthcare Application: Taxonomy, Substantially Analysis, Motivations, Challenges, Recommendations and Future Direction

Proposing New Blockchain Challenges in eHealth

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1 Introduction

Nowadays, healthcare is considered to be one of the most important human concerns. A lot of data related to healthcare are generated, stored, and reused frequently. One of the most important subsets of healthcare systems is Electronic Health Records (EHR). Electronic patient records provide many opportunities for healthcare stakeholders. For example, it allows medical records to be accessed by patients and avoids expensive tests, radiology, and repetitive imaging. Moreover, even if the patient is treated in different medical centers or in hospitals located in different cities, provinces, or other countries, physicians based in all those medical centers can access the patient’s records across far distances from each other using EHR. Another advantage of using EHR is having access to a history of medications used by the patient, which will help physicians in prescribing a new drug for the patient. Another advantage of using EHR is the use of patients’ medical records for research purposes and finding new treatment methods.

One of the basic challenges of using EHR in healthcare is how to preserve the patient’s privacy. With the wide access to patient records, the patients’ privacy is an important challenge. Another challenge for EHR is that the patient does not own his/her data and instead, it is the medical centers who own the patient’s data. Physicians and researchers can access a patient’s EHR without his or her consent to use these data for treatment and research purposes, and this is one aspect of the patient’s privacy. From a security perspective, using EHR brings up several challenges: First, the abundant use of IoT (Internet of Things) and wearable sensors to diagnose the disease and record data in the medical record of that patient can increase the risk of attacks. This could affect the physician’s prescription for the disease and endanger the patient’s life. The second security issue is fraud detection. There have been many cases where doctors have prescribed a drug for a patient that is not necessary for him/her just because that certain drug is available at the hospital’s pharmacy or medical center where the doctor works. As a result, the patient’s health may be compromised and/or the patient may be forced to bear unnecessary costs. Another security challenge is counterfeit drugs. Many people die of the use of counterfeit drugs or suffer from serious side effects from the use of these drugs. To address this challenge, a drug supply chain must be put in place in which critical information is accessible. This information must include the name of the pharmaceutical plant that has manufactured the drug, then where and how it has been stored; by what distributor it has been transported to the pharmacy, the distribution date, etc. To overcome the above-mentioned problems, Blockchain technology can be used. The distributed ledger of Blockchain has a distributable feature so it reduces the risk of an attack on an integrated center. Moreover, this distributed ledger cannot be changed and the transactions registered in it cannot be modified. In addition, only the patient can permit a third party to read or change their data by having their private key and public key. To do this systematic review, the guidelines proposed in [ 30 , 64 ] were adopted and the existing Blockchain-based approaches that tried to preserve privacy and security in healthcare are reviewed. The remaining of the paper is organized as follows. In Section 2 , previous review papers are discussed. Section 3 describes the methodology and criteria for selecting the papers. In Section 4 , the advantages and disadvantages of using Blockchain in the field of healthcare are discussed. The existing Blockchain-based approaches that tried to improve the privacy issue in healthcare are mentioned in Section 5 . The existing Blockchain-based approaches that tried to improve security issues in healthcare are described in Section 6 . In Section 7 , the reviewed papers are discussed and analyzed. Section 8 describes the open Issues and future research directions. Section 9 is dedicated to the conclusions and limitations of this SLR study.

2 Related work and motivation

In this section, we discuss the related survey and SLR papers that examined the Blockchain-based privacy and security approaches in healthcare.

The authors of [ 23 ] reviewed 143 papers on the role of Blockchain in healthcare and discussed the existing challenges in the EHR domain (including power consumption, failure, and attack points). Blockchain was used to solve these challenges of trustless environments and secure data exchange. In this paper two platforms were introduced: permissionless Blockchain (Ethereum) and permissioned Blockchain (Hyperledger) to solve EHR challenges. The authors reviewed the issues of privacy and security, and compared traditional EHR methods with those implemented by Blockchain. Finally, the limitations of the methods were mentioned.

In another study [ 55 ], 52 papers were reviewed. These papers discussed how Blockchain technology, along with smart contract systems, can support healthcare applications for physicians, patients, insurance companies, and assets such as patient’s data, medical information, equipment, and pharmaceutical chains.

The authors of [ 7 ] reviewed 31 papers. This paper described how this technology improves healthcare and prevents diseases and suggested a new protocol to ensure patient privacy and guarantee confidential data. Secure encryption methods and digital signatures were introduced to ensure authorized access to shared information using Blockchain. Then, a strong review of the accuracy of the EHR data was presented.

The authors of [ 31 ] reviewed 69 papers. This paper discusses the role of Blockchain in healthcare. This paper addressed the challenges of system security, interoperability, data sharing, and mobility in the field of EHR and explained how Blockchain can handle these challenges. Then, the following platforms were introduced to implement Blockchain in healthcare: Gem Health Network, OmniPHR, Medrec, Inclusive Social Networking System (PSN), and Virtual Resources.

Another study [ 54 ] was a systematic literature review that reviewed 42 papers published between 2016 and 2019 related to applying Blockchain in healthcare. In this paper, some challenges such as using Blockchain in healthcare, sharing and processing medical data and patient records were analyzed. The authors examined the implementation model, limitations, and costs of using Blockchain in healthcare.

The authors of [ 52 ] conducted a systematic review of 62 papers related to Blockchain-based approaches in healthcare systems published between 2016 and 2020. In this paper, the authors reviewed the use cases, challenges, and structures of Blockchain-based approaches in healthcare. Then, the implementation methods, technical cases, and the use of Blockchain in the field of medicine were evaluated. Finally, future directions and future works in this field were discussed.

The authors of [ 4 ] studied 37 papers related to Blockchain-based approaches in healthcare published between 2017 and 2020. This paper examined how to access medical records, security, data tracking, and medical information and how to exchange information in the Blockchain healthcare network. Also in this paper, challenges such as how to register and accept transactions, how to implement interoperability, regulations, and restrictions related to medical data in the community, and issues related to scalability and management of access permissions were mentioned.

The authors of [ 18 ] reviewed 39 papers that used Blockchain in healthcare approaches published between 2018 and 2020. This paper mentioned that using Blockchain can be effective for data integration, access control, and interoperability. The authors of this paper believed that using Blockchain in healthcare systems is expanding rapidly and therefore research in this field can be absolutely vital and useful.

Another study [ 19 ] reviewed a total number of 940 papers, and books published between 2016 and 2020 that used Blockchain technology in healthcare. This paper discussed telecare and the role of security and privacy. In this paper, some issues of using Blockchain in healthcare such as interoperability, scalability, and storage were discussed.

In another study [ 24 ], 50 papers published on reputable scientific sites between 2015 and 2020 that used Blockchain in healthcare were analyzed. This paper highlighted the role of quality criteria. First, new trends of using Blockchain in healthcare were introduced, then these new trends were analyzed, and finally, the challenges of using these new trends were discussed. This paper also discussed issues such as integrating cloud computing technology and Blockchain in healthcare.

The authors of [ 46 ] reviewed a total of 626 papers published between 2016 and 2020 that used Blockchain technology in healthcare. In this paper, systematic methods for reviewing papers were presented. These systematic methods include: relying on scientific methods, the number of authors of the paper per year, the introduction of the institutions that created the paper, and the separation of papers based on the country of the author of the paper.

Akbar et al. [ 5 ] reviewed 72 articles between 2017 and 2021 on the role of Blockchain in healthcare. In this research, the fuzzy technique has been used to prioritize and sort the existing solutions in the field of Blockchain-based healthcare. Also, in this research, new methods have been used to optimize and create a road map in the field of Blockchain-based healthcare.

Sharma et al. [ 51 ] reviewed 47 articles between 2017 and 2021 on the use of Blockchain in healthcare. In this research, challenges such as optimal use of resources, data integrity, and rapid development of the healthcare Blockchain have been addressed.

Rahmani et al. [ 42 ] reviewed 34 articles between 2016 and 2021 in the field of using Blockchain in the Internet Medical of Thing (IoMT). In this research, the challenges of trust in the context of cloud computing for storing Internet of Things data have been discussed. Blockchain is mentioned as a solution for decentralization and security of data generated by sensors and wearable devices.

The authors of [ 48 ] reviewed 51 articles between 2017 and 2021 on the use of Blockchain in the field of healthcare. In this research, the major challenges such as lack of integrity, manipulation, and fraud in medical care data have been identified, and Blockchain has been mentioned as a solution to overcome these challenges. Also, in this research, the benefits of using the Blockchain in the field of healthcare are mentioned, such as more efficiency, less delay in information transmission, more data security, and improved management of resource consumption.

Abbas et al. [ 1 ] reviewed 53 articles between 2016 and 2021 on the use of Blockchain technology in healthcare. In this article, advantages such as non-alteration and manipulation of healthcare data, anonymity of participating parties, protection of patients’ privacy, improvement of drug supply chain management, and safe and fast access to patient’s records in the healthcare Blockchain are mentioned.

Examining the mentioned papers, several defects are found. For example, some of these papers are not SLR or the selection process is not clear or the tools used for evaluation and the framework are not specified in these papers. In this systematic review, we attempted to address these shortcomings.

Table 1 lists survey and SLR papers on healthcare security and privacy using Blockchain in recent years. In this table, each paper is examined considering the publication year, main topic, review types, paper selection processes, tools or framework, and covered years.

3 Research methodology

In this section, a methodology for doing this systematic review is mentioned. A systematic literature review has several advantages over traditional reviews, including: greater transparency, more accurate reviews, step-by-step analyses, and more regular reviews. The article selection process and the research questions are also explained in this section.

3.1 Question formalization

The research questions that are answered in this study are as follows:

RQ1: What are the advantages and disadvantages of using Blockchain in Healthcare?

RQ2: How the patient’s privacy in EHR is guaranteed by Blockchain?

RQ3: How the patient’s security in EHR is guaranteed by Blockchain?

RQ4: What evaluation metrics are applied for evaluating the Blockchain-based approaches for improving security and privacy in healthcare?

RQ5: What are the tools or frameworks used in the Blockchain-based approaches for improving security and privacy in healthcare?

RQ6: What kind of Blockchain was used in the existing research studies?

RQ7: What are the open issues and future research directions of using Blockchain for improving the privacy and security of healthcare?

3.2 Paper selection process

Figure 1 summarizes the papers selection process in three steps:

At this step, the papers are selected based on the title, abstract and keywords. 487 papers were selected at the end of this step.

At this step, the continuation of the selection process of papers has been carried out based on the inclusion and exclusion criteria given in Table 3 . At the end of this step, 331 papers were remained.

Finally, by studying the full text of the papers and removing inappropriate ones, 51 papers were remained as final selected papers to be reviewed in this systematic review.

Paper selection process

This study reviews papers published between 2018 and August 2022 that focused on Blockchain-based approaches for improving security and privacy in healthcare. Various databases have been used to conduct this study. The URLs of the used database are listed in Table 2 .

The search keywords for the papers were as follows:

“Blockchain” AND (“Healthcare” OR “EHR” OR “Medicine” OR “Electronic Health Record”)

Table 3 lists the criteria for including and excluding the papers.

After applying the above keywords, 331 journal papers and 156 conference papers were found at the end of step 1. The number and percentage of journal and conference papers are shown in Fig. 2 .

Total selected papers at the end of step 1

Figure 3 illustrates the number and percentage of final papers selected from each database.

The number and percentage of final papers selected from each database

Figure 4 shows the number of final papers selected at the end of step 3 categorized by years.

The number of selected papers at the end of step 3 categorized by years

4 Advantages and disadvantages of using Blockchain in healthcare

In this section, we try to answer RQ1: What are the advantages and disadvantages of using Blockchain in Healthcare?

Using Blockchain technology can improve the integrity, privacy, and security and it provides better access to the necessary services. With Blockchain technology, both specialists and health organizations can act faster and more efficiently based on the available information which is safe and reliable. A safe and effective infrastructure can be created using smart contracts to increase the quality of healthcare and improve the well-being of individuals.

The authors of [ 47 ] presented the creation of the prototype and evaluation of the OmniPHR architectural model. A Personal Health Record (PHR) is a file that allows patients to access and manage their data. The OmniPHR integrates the Blockchain distributed records and OpenEHR. The performance of the OmniPHR was evaluated by dividing it into workloads and simultaneous sessions to transfer the database to a network of ten clouds. The results of the experimental evaluations in this paper showed that the Blockchain architecture of OmniPHR provides high-quality performance at the network level.

In another study [ 66 ], some applications of Blockchain in healthcare domains were presented as follows: (1) Track prescriptions to detect drug overdoses. (2) Sharing data for integrating traditional care into telemedicine. (3) Sharing data with the provider so that the patient can specify what data is being authorized. (4) Sharing the registered cases of cancer; collecting all of the observed cases of cancer. (5) Managing the patient’s digital identity to better match the patient’s history. (6) Creating a personal health record that can be fully accessed and controlled. (7) Automation of health insurance claims for error detection and fraud. This paper also discusses the challenges of using the Blockchain in healthcare, such as system evolution, privacy protection, etc.

Another study [ 29 ] mentioned some healthcare projects that benefit from Blockchain technology. One of the projects worth mentioning here is PokitDok. With PokitDok, organizations related to healthcare can implement modern business in Blockchain and a secure network of electronic health records and pharmaceutical equipment is provided.

In another study [ 60 ], a Blockchain-based security model was presented for electronic health records called EMRSB. In this model, medical data can be shared safely and effectively. By using Blockchain technology in EMRSB, Data loss and manipulation problems can be easily solved. Large files are stored in the IPFS file system Footnote 1 and the hash file is added to the Blockchain, which saves important resources in the Blockchain. This can increase the security level of the patient’s privacy information.

The authors of paper [ 27 ] believed that the decentralization of the Blockchain would safeguard healthcare data and preserve the privacy of stakeholders in the field. Another important point mentioned in this paper is the lower cost of transferring data in the Blockchain compared to traditional methods. Data transfer in the Blockchain is done without the use of a central entity, which makes it less costly. It also uses Blockchain data tracking to ensure that healthcare data comes from a reliable source.

In another study [ 34 ] the characteristics of data integration and the immutability of data in the Blockchain were mentioned, which makes the Blockchain a suitable platform for maintaining healthcare data. In the healthcare network implemented by the Blockchain, the data added to the ledger cannot be changed and manipulated. The decentralization of the Blockchain means that there is no single failure point for the healthcare network. The paper also referred to smart contracts that allow transactions and agreements to be drawn up between parties involved in the healthcare Blockchain network without third-party intervention.

Another study [ 62 ] listed several advantages of using Blockchain in healthcare, including: 1- Data accuracy in healthcare applications: Storing all healthcare data in the Blockchain makes this accompanying information up-to-date, traceable, and non-manipulative. These capabilities help medical professionals improve the treatment process of patients. 2- Interoperability of healthcare data: By using interoperability in the Blockchain network, the exchange of information between stakeholders in this field becomes better because all data in the Blockchain follow a certain standard, so the exchange of information is more efficient. 3- Data security in the field of healthcare: Capabilities such as hashing and data immutability in the Blockchain network make data healthcare more secure. 4- Lower cost of healthcare data management: The cost of data management in traditional healthcare data systems is much higher than storing these data seamlessly in a Blockchain network because the information is stored in different centers and databases. 5- Global sharing of healthcare data: A patient may be treated in one country and then travel to another to continue treatment. In this case, if traditional medical care systems are used, sharing patient’s data among several different countries will be very difficult and perhaps impossible. Using a Blockchain network, patient’s data can be easily shared globally. 6- Improving the audit of healthcare data using Blockchain: Using data audits in healthcare ensures that laws and regulations are fully complied with by institutions and stakeholders in this field. As data in the Blockchain is verifiable and information in the Blockchain is non-manipulative, it improves the audit of healthcare data.

The authors of [ 33 ] believed that wearable devices and patient-connected sensors play an important role in modern healthcare systems. In this paper, the data generated from these devices are integrated with Blockchain technology. This integration plays an important role in maintaining the security of this data.

The authors of [ 59 ] dealt with parallel healthcare systems (PHSs) and the role of Blockchain in maintaining data security of these systems. This paper proposed a method in which Blockchain is combined with PHS and using a consortium, healthcare data is shared more effectively.

Another study [ 20 ] pointed out some of the challenges in using Blockchain in healthcare, including high energy consumption, inefficient scalability, and relatively low throughput. To address these challenges, this paper introduced an architecture called lightweight Blockchain. In a lightweight Blockchain network, nodes were distributed in several clusters and a ledger was maintained in each cluster. This reduced the computational and communication costs of the healthcare network.

In [ 53 ], the attribute-based signature scheme was introduced to further protect the privacy of medical stakeholders. In this design, keys called master key to authenticate users and update key to specify attributes related to certain nodes were introduced. In this scheme, a number of parties participating in the Blockchain network (such as physicians) were identified with certain characteristics (such as < Hospital A. Department of Oncology. senior Physician>. After analyzing the patient information, these attributes are taken away from them by an algorithm called KUNodes.

Jeet et al. [ 21 ] developed a Blockchain-based framework for IoT data. In this framework, patient’s data were collected by sensors and wearable devices, and were updated every moment. Therefore, new symptoms of illness and sensitivity in response to drugs can be recorded in the Blockchain immediately. Sha-256 encryption was used in this framework and the techniques used in this research reduce the encryption time.

Rajasekaran and Azees [ 43 ] presented a scheme for authentication of participating parties in the healthcare Blockchain. This scheme is a lightweight authentication scheme that supports the anonymity of participating parties in the healthcare Blockchain. In this scheme, doctors given the opportunity to share information about patients with other doctors without compromising the privacy and security of patient’s data. Using the authentication method of this scheme, only authorized users can view the data of the healthcare field.

The authors of [ 69 ] presented a scheme for secure storage and sharing of medical data based on Blockchain. In this research, the authentication of all parties involved in the healthcare system has been carefully examined and a solution to the problem of information dispersion in the healthcare field has been provided.

5 Privacy in healthcare using the Blockchain

In this section, we try to answer RQ2: How the patient’s privacy in EHR is guaranteed by Blockchain?

Blockchain technology can create a balance between the privacy of health data and access to those data. The purpose of the privacy policy is to protect patients’ privacy while disclosing PHI Footnote 2 . Four goals must be achieved here: 1- Giving t full control of EHRs to patients. 2- Determining who can access and track the documents. 3- Making possible the secure transfer of the records. 4- Minimizing the chance of unauthorized people obtaining PHI. Blockchain technology can help achieve these four goals.

In [ 12 ], the authors recommended an efficient and secure Blockchain-based framework for accessing medical records called Ancile. Smart contracts in this framework were used for controlling and preventing data misuse. In addition, for improving security, advanced encryption techniques were applied. The purpose of this paper is to address privacy and security issues in healthcare. This framework focused on the rights of patient’s data ownership. Data ownership is held by the patient, while parental or caregiver control is provided.

Paper [ 63 ] mentioned that in modern healthcare systems, patient participation is an important matter. This paper discussed Blockchain-based location sharing for E-health systems. The first step defines the basic needs for Blockchain-based location sharing, including decentralization, privacy, and reliability. Then, using Merkel’s cryptography and root, a Blockchain-based privacy-preserving scheme called BMPLS was proposed for Location Sharing Footnote 3 . The results showed that this plan meets the necessary requirements. Finally, the outputs of this project and the results of the analysis confirm that this project is useful and feasible for the field of medical care. In short, the scheme could be used to share telecare Blockchain-based privacy for medical information systems.

In another study [ 66 ], Healthchain, a large-scale Blockchain-based health data privacy project was presented, in which health data were encrypted to control micro-access. With the introduction of the Healthchain, IoT data and physician diagnoses cannot be deleted or manipulated. Security analysis and experimental results suggest that Healthchain’s proposal applies to the smart healthcare system. The important points mentioned in this paper are as follows: 1- A Blockchain-based healthcare system is recommended to protect the privacy of large-scale health data, called a Healthchain. The Healthchain allows users to download IoT data and receive feedback from physicians. Physicians are then able to read data and upload feedbacks. 2- In the Healthchain, for reducing the computational overhead and ensuring privacy, data is encrypted and stored in the IPFS Footnote 4 . 3- In addition, by transferring updated transactions, Healthchain allows users to revoke physicians’ access at any time.

In another study [ 39 ], a Blockchain-based data storage scheme in healthcare was proposed. The proposed scheme can help improve privacy. Encryption techniques were used to protect patient’s data and alias. In this approach, data processing methods as well as the cost-effectiveness of smart contracts used in the system were analyzed. Patients and health organizations participate as data transmitters and data receivers. With the assistance of these EHR systems and storing data in cloud network, patients share their personal data with physicians and health organizations.

The authors of [ 50 ] proposed a plan for implementation of EHR, which would protect EHR data more securely and privately. In this design, a framework was introduced that used the Hyperledger Fabric Blockchain.

In the proposed platform in [ 40 ], many problems are solved by storing encrypted health information in the cloud system. This platform ensures that patient’s data in the cloud environment is controlled only by the patient himself. The goal is to maintain important healthcare data for network integrity and security. Current health systems do not have a pseudonym because they only store data in the cloud environment. But the proposed platform guarantees patients’ aliases. Acquired aliases are obtained using cryptographic functions.

The proposed approach in [ 15 ] used four technologies that could be used in Blockchain for improving privacy. These four technologies are: zero-knowledge proofs, trusted execution environments, homomorphic encryption, and federal learning. In zero-knowledge proofs, one party involved (the prover) is allowed to validate a transaction or validation for the other party (the verifier) without disclosing any critical information. In healthcare contexts, for example, how a patient is treated can be expressed without disclosing the patient’s true identity. In federal learning, an algorithm is sent to a node, then that node analyzes the algorithm and finally shares the updated algorithm among all the nodes in the Blockchain. In this way, by separating how to update the algorithm from other nodes, the risks of privacy and security breaches are minimized. Homomorphic encryption allows calculations to be performed on encrypted data. For example, a patient can encrypt their data and send it to an unreliable third party. This third party performs an analysis on the encrypted data and then sends the result of its analysis to the patient in an encrypted form. In this way, the patient can utilize another person’s review of their data without exposing his/her data. In trusted execution environment technology, privacy is met through hardware. Most cell phones today use this technology in their structure.

In [ 44 ], Blockchain-based knapsack algorithms were used for privacy. The greedy algorithm of knapsack can lead to Blockchain-based privacy and security in healthcare. In this method, first the healthcare data is encrypted by the knapsack algorithm and then this encrypted data is transferred to the Blockchain. In the Blockchain, healthcare data is validated and then decrypted by the knapsack algorithm and finally sent to the desired nodes. Knapsack algorithms are symmetric cryptographic systems. This method uses public keys to encrypt and private keys to decrypt.

Paper [ 32 ] suggested a framework that uses off-chain computing and storage technology. Off-chain Blockchain hybrid design architecture (OCBS) processes and manages information through distributed software that interacts with off-chain sources. This system tries to improve privacy and scalability. In the framework proposed in this paper, the ownership rights of patient’s data are observed. Moreover, in this framework, patients can manage their own data and digital identity.

Paper [ 67 ] proposed a Blockchain-based telephone privacy tracking plan in the field of healthcare. In this plan, healthcare stakeholders can connect to the Blockchain network with their mobile phones. In this plan, first, the location of the caller is determined and then it is determined whether a particular patient has called this system. In the design proposed in this paper, the integration of emerging 5G technology with Blockchain-based healthcare systems leads to higher reliability, less communication delay and improved privacy of medical stakeholders.

Another study [ 58 ] pointed to the role of Blockchain technology in better management of healthcare data and maintaining the security of this data. In this paper, a prototype using the Hyperledger platform was proposed. This prototype was an authorized private Blockchain that ensures better control of access to healthcare data.

The paper [ 3 ] proposed a reliable framework for wearable devices and patient-connected sensors that utilized Blockchain technology. With data management, this framework protected the privacy of information related to the field of healthcare and ensured the confidentiality and integrity of data.

The authors of [ 25 ] introduced a framework that uses Blockchain technology. Using smart contracts, this framework provided effective management to conserve human resources. In this framework, the human resource data were created and then these data were distributed on a global platform based on Blockchain.

In another study [ 65 ], fuzzy analytic on the blockchain platform was introduced. Using fuzzy analytic network, a Blockchain implementation model to improve the security of healthcare data was introduced. In this study, a permissioned private Blockchain network was used to manage access to medical data.

A decentralized architecture based on Blockchain was proposed by Nishi et al. [ 38 ]. In this architecture, the patient is the real owner of his/her data, in such a way that any permission to view the data related to the patient must be done with his/her permission. In this architecture, attribute authorities can issue or revoke the attribute only with the patient’s permission.

Alsayegh et al. [ 8 ] investigated how the privacy and security of EHR sharing can be maintained in two types of Blockchain networks. Private Blockchain was used to store encrypted EHRs and consortium Blockchain along with smart contracts to verify the identity of patients.

The authors of [ 2 ] introduced a framework for greater security and privacy of individuals who received the Covid-19 vaccine using Blockchain. In this framework, the W3C standard certificate is used to prove the certificate of receiving the vaccine. In this framework, IPFS has also been used to protect the privacy of vaccine recipients. In this framework, users have been given the opportunity to share their data with other people without compromising their security and privacy.

6 Securing healthcare data by using the Blockchain

In this section, we try to answer RQ3: How the patient’s security in EHR is guaranteed by Blockchain?

In the smart health scenario, one of the most important issues is the security of the health system. The main challenges for a smart health system are security and the reduction of accurate data with the rule. Blockchain technology suggests that a consortium shall consist of several stakeholders such as hospitals, physicians, pharmacists, pathologists, researchers, and insurance companies. The security debate here means the secure exchange of data among all the parties involved. Moreover, all the stakeholders must be authenticated and authorized to enter each level of the Blockchain.

The authors of [ 26 ] stated that Blockchain may provide a solution to address current EHR performance limitations. In Blockchain, the patient’s entire record is stored in the ledger and encrypted by the patient’s private key. Although the Blockchain system is not completely impenetrable, it is more secure than most current systems.

The authors of [ 11 ] suggested that data theft in the EHR can endanger patient privacy. In general, most data in the EHR remains unchanged after being uploaded to the system. Therefore, Blockchain can be used to share this data more effectively. Participating organizations and medical parties can more confidently access EHRs stored in Blockchain. In this paper, a cryptographic scheme for healthcare was proposed based on Blockchain technology. The index for the EHR is stored in the Blockchain. Because only this index is transferred to Blockchain for ease of publication, patients have complete control over who can view their EHR data. In this system, only search indices are added to the Blockchain and facilitate EHR distribution, while real EHRs are stored encrypted on another server. To access EHRs, users must grant their permission to the information owner with a decryption key.

Paper [ 36 ] presented a new EHR sharing scheme based on cloud computing and Blockchain. Initially, the authors identified the main challenges of current health systems, and effective solutions to these problems are proposed through the implementation of a real prototype. To test the proposed method, an Amazon-based Ethereum Blockchain is proposed. Moreover, to achieve data storage and data sharing, the IPFS storage system integrates with Blockchain. The results of this program showed that the proposed framework can share medical information more safely and quickly compared to conventional methods. By using access control, unauthorized access to health data can be detected and prevented. The advantages of the proposed model showed that the Blockchain solution is a more effective way to manage medical records compared to traditional methods.

Paper [ 28 ] addressed the problems of data collaboration and the use of healthcare programs in a heterogeneous cloud environment. A framework called ChainSDI suggests that the Blockchain technique, along with many computational resources, may be used to manage secure data. The prototype shows how this framework works.

The proposed method in [ 10 ] had the following architecture contributions: First, a healthcare framework called ChainSDI is presented which is based on a combined “home-edge-core” SDI to provide real-time performance and accountability for home-based healthcare services. Second, they are looking to build a secure Blockchain network to ensure that any transaction in ChainSDI is in accordance with the regulations, while still being able to interact with the data.

Paper [ 17 ] provided telemedicine services on demand (MoD). This technology is used to overcome challenges and improve telemedicine services. This paper proposed an approach to achieve authentication and licensing with greater flexibility and efficiency for the department of defense’s services in the medical trap system. A key program has been distributed for independent updates in the telemedicine system, which aims to update the patient’s keys separately. Using Blockchain and distributed ledger also protects the integrity of private healthcare data. This prevents malicious users from trying to change the physicians’ diagnosis. Using the Blockchain technique in EHR, patient’s data is stored in a chain to prevent a user or unauthorized users from manipulating it. Finally, it is concluded that the proposed approach resists collusion attacks in (N-1) destructive attacks.

In [ 22 ], containers in the Blockchain substrate were used for greater security of healthcare data. These containers are connected to multiple ports to improve the data transfer process. In this research, a framework called Medichain on a Blockchain platform is proposed. In each block of the proposed framework, a list of patient records is maintained, which is secured using the security features of Blockchain technology. This framework was implemented by the Python programming language and used object-oriented concepts.

The authors of [ 61 ] used Blockchain technology to further secure healthcare data. The scheme proposed in this paper places great emphasis on protecting patients’ medical records from information theft and unauthorized intrusion. This paper first identified how to manage and control access to medical care data. Then, using Blockchain technology, a platform for data storage and transmission was introduced. In this platform, data transfer and storage were done through cryptographic algorithms. The results of the implementation and simulation of the proposed platform showed better performance in data storage as well as more efficient data transfer than similar schemes.

The authors of [ 68 ] emphasized the privacy and security issues of healthcare stakeholders. In this paper, several features of Blockchain technology such as: anonymous signatures, zero-knowledge proofs, attribute-based encryption, and approval of smart contracts were used for more security of healthcare data. This paper also used various security techniques to ensure the data sharing process.

In another study [ 45 ], the characteristics of the Blockchain network were investigated. Then, consensus algorithms were analyzed, and finally, a framework for maintaining the security and privacy of data related to patients in the field of healthcare was introduced.

The authors of [ 57 ] discussed remote patient monitoring (RPM). In this paper, an architecture was presented that effectively transfers healthcare data and stores them in a Blockchain.

In another study [ 14 ], Blockchain’s smart contracts were used for the proper analysis and management of data generated in the field of medical care. Using the method presented in this paper, the generated data by sensors connected to the patient’s body are analyzed by smart contracts. If the patient-generated data were in critical condition, a warning was sent to the medical center so that the patient could receive immediate intensive care.

In [ 13 ], a Blockchain-based healthcare data management system was proposed. Using this information management system, patients can easily access their medical records located in various medical centers. Asymmetric encryption was used to further secure the system data.

The authors of [ 56 ] integrated smart health care systems (SHSs) with Blockchain technology. This paper examined the challenges of SHS systems and used Blockchain technology to maintain greater security and data integrity in the field of smart healthcare.

Another study [ 16 ] presented an attribute-based signature scheme with different authorities. In this paper, the patient disclosed part of his data without exposing the rest of his information. This part of the information disclosed by the patient is provided to physicians and researchers by healthcare providers. The physician or researcher performs the desired analysis on this data. At the end, these authorities were taken away from them.

The authors of [ 41 ] proposed solutions to prevent the production and distribution of counterfeit drugs in the healthcare network using Blockchain technology. This plan covers the drug distribution cycle from production to consumption by the patient. The distribution and production of counterfeit drugs in the healthcare system is prevented by using Blockchain.

Paper [ 35 ] dealt with the safe storing of healthcare data. It provided a Blockchain-based framework using a keyless signature protocol for the security of patient’s medical records and ensured the integrity and security of data in this area.

Another study [ 49 ] introduced a framework based on Blockchain. In this framework, the management and control of access to medical data were effectively proposed. The use of this framework improved data privacy, confidentiality, and decentralization in the medical care system.

Qadar Butt et al. [ 9 ] presented a Blockchain technology for use in medical communication and developed a location-independent global health record exchange system for transferring medical data. Using Blockchain technology and a federal identity management system, the proposed system authenticates users and the person requiring user information under the guidance.

The authors of [ 37 ] presented a scheme for sharing data in the field of healthcare using Blockchain and edge computing. This scheme guarantees the security and privacy of shared data. In this scheme, the hash and filtering functions were used to maintain the security of the shared data. Also, in this research, a process has been designed to determine the amount of reward for miners to mine healthcare blocks.

In [ 6 ], Blockchain was used to access keywords for searching in distributed healthcare databases and a new mechanisms are used to revoke the public and private keys of users. Therefore, any user will not be able to access the healthcare blockchain after a certain period of time. This makes the healthcare Blockchain more secure. In the proposed approach, public and private keys are given to the participating parties only for a certain period of time to prevent unauthorized people from entering the healthcare Blockchain.

7 Discussion

This section analyzes the reviewed papers to answer the remaining research questions:

RQ4: What evaluation metrics are applied for evaluating the Blockchain-based approaches for improving security and privacy in healthcare?

Table 4 lists the evaluation metrics for assessing the Blockchain-based approaches for improving security and privacy in healthcare. Evaluation metrics such as integrity (in 10% of papers), access control (in 8% of papers), security (in 25% of papers), privacy (in 17% of papers), availability (in 6% of papers), latency (in 4% of papers), scalability (in 10% of papers), performance (in 17% of papers) and cost (in 4% of papers) were reviewed and analyzed. Figure 5 represents the percentage of using each evaluation metrics considered in the selected papers.

The percentage of using each evaluation metric considered in the selected papers

RQ5: What are the tools or frameworks used in the Blockchain-based approaches for improving security and privacy in healthcare?

Table 4 lists the tools and frameworks used in the existing Blockchain-based approaches for improving security and privacy in healthcare. Various frameworks, platforms and tools have been used in the papers reviewed in this review paper. These frameworks, platforms and tools have various features, the most important of which are: Use of smart contracts to control access and protection of data, guaranteeing access to data and ensuring that the patient owns the information about himself/herself, protection of data generated by sensors and wearable devices, distribution of data globally and Internationally, artificial intelligence decision making for better disease diagnosis, searchable encryption for sharing medical records, secure management of healthcare data, telemedicine services, etc.

RQ6: What kind of Blockchain was used in the existing research studies?

Table 4 lists the types of Blockchains used in each paper. Figure 6 represents the percentage of the Blockchain’s type, used in each reviewed paper. 35% of the reviewed papers used private Blockchain, 10% used hybrid Blockchain, 43% used public Blockchain, and 12% used consortium Blockchain.

The percentage of Blockchain’s type used in the reviewed papers

8 Open issues and future research directions

In this section, we aim to answer RQ7: What are the open issues and future research directions of using Blockchain for improving the privacy and security of healthcare?

Some issues of using Blockchain in Healthcare such as cost, profitability, and scalability require further research. Using a distributed system for eliminating intermediaries can effectively overcome many of the current challenges in the medical and healthcare systems. Moreover, despite the existence of a pandemic such as Corona (Covid-19), the creation of a Blockchain network, which is a consortium of all the parties involved in the disease, could be the subject of future research. Using this Blockchain consortium network, various medical centers, governments, patients, insurance companies, information centers, etc. can exchange all information about epidemics. Therefore, by using this safe platform, all treatment methods as well as accurate statistics of epidemic diseases, can be obtained. Some other important open issues and future works are:

Pharmacy: The use of Blockchain in the pharmaceutical industry improves the tracking of products in this area and prevents the distribution of counterfeit drugs.

Globalization of healthcare networks: Blockchain-based healthcare networks can be implemented globally. Using global healthcare networks, patients’ medical records can be accessed from anywhere in the world.

Improving the scalability of Blockchain-based healthcare: Due to the increasing use of Blockchain technology in healthcare networks, more research is needed to improve the scalability of these networks.

Use more efficient cryptographic techniques: Healthcare transactions contain critical information that is considered by many hackers and attackers. Therefore, the development of new and more effective encryption methods requires more researches.

Use of artificial intelligence in Blockchain-based healthcare networks: As Blockchain-based healthcare systems are growing exponentially; analyzing data in this area will become increasingly difficult. Using artificial intelligence and machine learning can make it easier to parse and analyze data in this area.

9 Conclusion and limitation

This review provided a systematic review of the existing Blockchain-based approaches that tried to preserve privacy and security in healthcare. At first, Blockchain and its characteristics were defined, and then the electronic health records and the role that Blockchain can play in maintaining security and privacy in this area were examined. We selected and reviewed recent papers from valid scientific databases. The advantages and disadvantages of using Blockchain in healthcare compared to traditional methods were mentioned. After applying the mentioned query, 331 journal papers and 156 conference papers were found in all of the above-mentioned databases. Finally, we selected 51 papers published between 2018 and December 2022 according to the mentioned paper selection process. We discussed the main idea, evaluation metrics, and tools or framework, and type of Blockchain used in each selected paper. Evaluation metrics such as integrity (in 10% of papers), access control (in 8% of papers), security (in 25% of papers), privacy (in 17% of papers), availability (in 6% of papers), latency (in 4% of papers), scalability (in 10% of papers), performance (in 16% of papers) and cost (in 4% of papers) were used in the reviewed papers. Regarding the type of Blockchain used in the papers, it was observed that 35% of the reviewed papers used private Blockchain, 10% used hybrid Blockchain, 43% used public Blockchain and 12% used consortium Blockchain.

Regarding the limitations of this paper, we can mention the non-use of conference papers. Conference papers can sometimes contain interesting and innovative materials. In this paper, seven research questions were mentioned and answered, while other researchers may consider additional questions. Also in this review paper, six valid scientific databases were used to search for papers, while other valid scientific databases were also available for search. In this paper, only international journals have been used and national and domestic journals have been omitted. Moreover, non-English papers and book chapters were not used. Finally, this paper reviewed papers that were published between 2018 and August 2022, and papers that were published before 2018 were not reviewed.

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Kiania, K., Jameii, S.M. & Rahmani, A.M. Blockchain-based privacy and security preserving in electronic health: a systematic review. Multimed Tools Appl 82 , 28493–28519 (2023). https://doi.org/10.1007/s11042-023-14488-w

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1. Introduction

3. Open Science

3.1. Overview

3.2. Requirement Analysis for an Infrastructure

4. Blockchain Technology

4.1. Overview

4.2. Blockchain Technology as an Open Science Infrastructure

5. State-of-the-Art

5.1. Research Overview

5.2. Literature

5.3. Projects

5.3.1. Social Research Platform/Repository

5.3.2. Reproducibility

5.3.3. Transparent Evidence

5.3.4. Intellectual Property Protection

5.3.5. Resource Sharing

5.3.6. Customizable Infrastructure

5.4. Summary and Discussion

6. Challenges and Research Potentials

6.1. Risks and Validation of Smart Contracts

6.2. Missing Standardization and Frameworks

6.3. Incentive Systems for Science

6.4. Scientific Metrics

6.5. Legal Uncertainties

7. Conclusions

Author Contributions

Conflict of Interest

Supplementary Material

This article is part of the Research Topic

Where Is Current Research on Blockchain Technology?—A Systematic Review

Introduction

Research methodology

Definition of research questions

Conducting the search

Screening of relevant papers

Keywording on the basis of the abstract

Data extraction and mapping process

Basic information of the papers

Search and selection results

Publication year, source and geographic distribution

Publication type and channel

Classification of the relevant papers

Wasted resources

Throughput, latency, size and bandwidth, and versioning, hard forks, multiple chains

Smart contracts, new cryptocurrencies, botnet, broadcast protocol, trustworthiness

Summary of the identified challenges/limitations and suggested solutions in Blockchain

RQ1: What research topics have been addressed in current research on Blockchain?

RQ2: What applications have been developed with and/to Blockchain technology?

RQ3: What are the current research gaps in Blockchain research?

RQ4: What are the future research directions for Blockchain?

Limitations of the systematic mapping study

Supporting Information

S1 Checklist. PRISMA Checklist.

S1 Diagram. PRISMA Flow diagram.

Author Contributions

Blockchain-based privacy and security preserving in electronic health: a systematic review

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