research paper binary search

Efficient Privacy-Preserving Binary Tree Search

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• First Online: 11 November 2020
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• Ruidi Yang   ORCID: orcid.org/0000-0003-1012-4595 12 &
• Maohua Sun   ORCID: orcid.org/0000-0002-7474-7268 12 , 13  

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12486))

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• International Conference on Machine Learning for Cyber Security

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Binary search tree is a common structure in computer programming and is currently widely used in many scenarios, such as routing search engine in routers, massive data concurrent query, set/multiset, map in C++ STL, and Linux Such technology is applied to memory management and the like. However, with the development of network technology, people’s demand for privacy protection has gradually increased. The leakage of many key information will cause serious consequences, and also restrict the application and development of network technology. In the process of binary tree search, it is easy to have such a problem: the searched party may only want to provide the location of the searched data without exposing all the data in the entire binary search tree; at the same time, the searcher does not want to expose itself The information you need to find. This leads to issues related to the privacy protection of binary search trees. In response to the above problems, we have proposed a binary tree search solution that can protect privacy, which is used to solve the data leakage problem that may exist during the search process of the binary tree. The feature of the scheme proposed in the text is that it integrates two technologies of binary search tree and obfuscation circuit, so as to effectively protect the binary search tree and perform the search without leaking data. And the security analysis shows that the protocol we proposed is very safe and efficient, which can protect the data beneficially.

• Binary search tree; privacy-preserving
• Oblivious transfer
• Garbled circuit

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School of Management and Engineering, Capital University of Economics and Business, Beijing, 100070, China

Ruidi Yang & Maohua Sun

Project of High-Level Teachers in Beijing Municipal Universities in the Period of 13th Five-Year Plan (CN), Grant Numbers: CIT&TCD201904097; CUEB Supporting Plan for the Youth Backbone Teachers, Beijing, China

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Xidian University, Xi'an, China

Xiaofeng Chen

Guangzhou University, Guangzhou, China

Hongyang Yan

Michigan State University, East Lansing, MI, USA

Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

Xiangliang Zhang

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Yang, R., Sun, M. (2020). Efficient Privacy-Preserving Binary Tree Search. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_37

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Binary Search Trees

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Fernanda Argota

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Will Knight

Apple’s MM1 AI Model Shows a Sleeping Giant Is Waking Up

While the tech industry went gaga for generative artificial intelligence , one giant has held back: Apple. The company has yet to introduce so much as an AI-generated emoji, and according to a New York Times report today and earlier reporting from Bloomberg, it is in preliminary talks with Google about adding the search company’s Gemini AI model to iPhones .

Yet a research paper quietly posted online last Friday by Apple engineers suggests that the company is making significant new investments into AI that are already bearing fruit. It details the development of a new generative AI model called MM1 capable of working with text and images. The researchers show it answering questions about photos and displaying the kind of general knowledge skills shown by chatbots like ChatGPT. The model’s name is not explained but could stand for MultiModal 1. MM1 appears to be similar in design and sophistication to a variety of recent AI models from other tech giants, including Meta’s open source Llama 2 and Google’s Gemini . Work by Apple’s rivals and academics shows that models of this type can be used to power capable chatbots or build “agents” that can solve tasks by writing code and taking actions such as using computer interfaces or websites. That suggests MM1 could yet find its way into Apple’s products.

“The fact that they’re doing this, it shows they have the ability to understand how to train and how to build these models,” says Ruslan Salakhutdinov , a professor at Carnegie Mellon who led AI research at Apple several years ago. “It requires a certain amount of expertise.”

MM1 is a multimodal large language model, or MLLM, meaning it is trained on images as well as text. This allows the model to respond to text prompts and also answer complex questions about particular images.

One example in the Apple research paper shows what happened when MM1 was provided with a photo of a sun-dappled restaurant table with a couple of beer bottles and also an image of the menu. When asked how much someone would expect to pay for “all the beer on the table,” the model correctly reads off the correct price and tallies up the cost.

When ChatGPT launched in November 2022, it could only ingest and generate text, but more recently its creator OpenAI and others have worked to expand the underlying large language model technology to work with other kinds of data. When Google launched Gemini (the model that now powers its answer to ChatGPT ) last December, the company touted its multimodal nature as beginning an important new direction in AI. “After the rise of LLMs, MLLMs are emerging as the next frontier in foundation models,” Apple’s paper says.

MM1 is a relatively small model as measured by its number of “parameters,” or the internal variables that get adjusted as a model is trained. Kate Saenko , a professor at Boston University who specializes in computer vision and machine learning, says this could make it easier for Apple’s engineers to experiment with different training methods and refinements before scaling up when they hit on something promising.

Saenko says the MM1 paper provides a surprising amount of detail on how the model was trained for a corporate publication. For instance, the engineers behind MM1 describe tricks for improving the performance of the model including increasing the resolution of images and mixing text and image data. Apple is famed for its secrecy, but it has previously shown unusual openness about AI research as it has sought to lure the talent needed to compete in the crucial technology.

Adrienne So

Saenko says it’s hard to draw too many conclusions about Apple’s plans from the research paper. Multimodal models have proven adaptable to many different use cases. But she suggests that MM1 could perhaps be a step toward building “some type of multimodal assistant that can describe photos, documents, or charts and answer questions about them.”

Apple’s flagship product, the iPhone, already has an AI assistant—Siri. The rise of ChatGPT and its rivals has quickly made the once revolutionary helper look increasingly limited and out-dated. Amazon and Google have said they are integrating LLM technology into their own assistants, Alexa and Google Assistant. Google allows users of Android phones to replace the Assistant with Gemini. Reports from The New York Times and Bloomberg that Apple may add Google’s Gemini to iPhones suggest Apple is considering expanding the strategy it has used for search on mobile devices to generative AI. Rather than develop web search technology in-house, the iPhone maker leans on Google, which reportedly pays more than $18 billion to make its search engine the iPhone default. Apple has also shown it can build its own alternatives to outside services, even when it starts from behind. Google Maps used to be the default on iPhones but in 2012 Apple replaced it with its own maps app .

Apple CEO Tim Cook has promised investors that the company will reveal more of its generative AI plans this year. The company faces pressure to keep up with rival smartphone makers, including Samsung and Google, that have introduced a raft of generative AI tools for their devices.

Apple could end up tapping both Google and its own, in-house AI, perhaps by introducing Gemini as a replacement for conventional Google Search while also building new generative AI tools on top of MM1 and other homegrown models. Last September, several of the researchers behind MM1 published details of MGIE , a tool that uses generative AI to manipulate images based on a text prompt.

Salakhutdinov believes his former employer may focus on developing LLMs that can be installed and run securely on Apple devices. That would fit with the company’s past emphasis on using “on-device” algorithms to safeguard sensitive data and avoid sharing it with other companies. A number of recent AI research papers from Apple concern machine-learning methods designed to preserve user privacy. “I think that's probably what Apple is going to do,” he says.

When it comes to tailoring generative AI to devices, Salakhutdinov says, Apple may yet turn out to have a distinct advantage because of its control over the entire software-hardware stack. The company has included a custom “neural engine” in the chips that power its mobile devices since 2017, with the debut of the iPhone X. “Apple is definitely working in that space, and I think at some point they will be in the front, because they have phones, the distribution.”

In a thread on X, Apple researcher Brandon McKinzie, lead author of the MM1 paper wrote : “This is just the beginning. The team is already hard at work on the next generation of models.”

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Research Identifies Characteristics of Cities That Would Support Young People’s Mental Health

Survey responses from global panel that included young people provide insights into what would make cities mental health-friendly for youth

As cities around the world continue to draw young people for work, education, and social opportunities, a new study identifies characteristics that would support young urban dwellers’ mental health. The findings, based on survey responses from a global panel that included adolescents and young adults, provide a set of priorities that city planners can adopt to build urban environments that are safe, equitable, and inclusive. 

To determine city characteristics that could bolster youth mental health, researchers administered an initial survey to a panel of more than 400, including young people and a multidisciplinary group of researchers, practitioners, and advocates. Through two subsequent surveys, participants prioritized six characteristics that would support young city dwellers’ mental health: opportunities to build life skills; age-friendly environments that accept young people’s feelings and values; free and safe public spaces where young people can connect; employment and job security; interventions that address the social determinants of health; and urban design with youth input and priorities in mind. 

The paper was published online February 21 in  Nature .

The study’s lead author is Pamela Collins, MD, MPH, chair of the Johns Hopkins Bloomberg School of Public Health’s Department of Mental Health. The study was conducted while Collins was on the faculty at the University of Washington. The paper was written by an international, interdisciplinary team, including citiesRISE, a global nonprofit that works to transform mental health policy and practice in cities, especially for young people.

Cities have long been a draw for young people. Research by UNICEF projects that cities will be home to 70 percent of the world’s children by 2050. Although urban environments influence a broad range of health outcomes, both positive and negative, their impacts manifest unequally. Mental disorders are the leading causes of disability among 10- to 24-year-olds globally. Exposure to urban inequality, violence, lack of green space, and fear of displacement disproportionately affects marginalized groups, increasing risk for poor mental health among urban youth.

“Right now, we are living with the largest population of adolescents in the world’s history, so this is an incredibly important group of people for global attention,” says Collins. “Investing in young people is an investment in their present well-being and future potential, and it’s an investment in the next generation—the children they will bear.” 

Data collection for the study began in April 2020 at the start of the COVID-19 pandemic. To capture its possible impacts, researchers added an open-ended survey question asking panelists how the pandemic influenced their perceptions of youth mental health in cities. The panelists reported that the pandemic either shed new light on the inequality and uneven distribution of resources experienced by marginalized communities in urban areas, or confirmed their preconceptions of how social vulnerability exacerbates health outcomes. 

For their study, the researchers recruited a panel of more than 400 individuals from 53 countries, including 327 young people ages 14 to 25, from a cross-section of fields, including education, advocacy, adolescent health, mental health and substance use, urban planning and development, data and technology, housing, and criminal justice. The researchers administered three sequential surveys to panelists beginning in April 2020 that asked panelists to identify elements of urban life that would support mental health for young people.

The top 37 characteristics were then grouped into six domains: intrapersonal, interpersonal, community, organizational, policy, and environment. Within these domains, panelists ranked characteristics based on immediacy of impact on youth mental health, ability to help youth thrive, and ease or feasibility of implementation. 

Taken together, the characteristics identified in the study provide a comprehensive set of priorities that policymakers and urban planners can use as a guide to improve young city dwellers' mental health. Among them: Youth-focused mental health and educational services could support young people’s emotional development and self-efficacy. Investment in spaces that facilitate social connection may help alleviate young people’s experiences of isolation and support their need for healthy, trusting relationships. Creating employment opportunities and job security could undo the economic losses that young people and their families experienced during the pandemic and help cities retain residents after a COVID-era exodus from urban centers.  

The findings suggest that creating a mental health-friendly city for young people requires investments across multiple interconnected sectors like transportation, housing, employment, health, and urban planning, with a central focus on social and economic equity. They also require urban planning policy approaches that commit to systemic and sustained collaboration, without magnifying existing privileges through initiatives like gentrification and developing green spaces at the expense of marginalized communities in need of affordable housing.

The authors say this framework underscores that responses by cities should include young people in the planning and design of interventions that directly impact their mental health and well-being. 

“ Making cities mental health friendly for adolescents and young adults ” was co-authored by an international, interdisciplinary team of 31 researchers led by the University of Washington Consortium for Global Mental Health, Urban@UW, the University of Melbourne, and citiesRISE. Author funding is listed in the Acknowledgements section of the paper.

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Astrophysics > High Energy Astrophysical Phenomena

Title: the mass density of merging binary black holes over cosmic time.

Abstract: The connection between the binary black hole (BBH) mergers observed by LIGO-Virgo-KAGRA (LVK) and their stellar progenitors remains uncertain. Specifically, the fraction $\epsilon$ of stellar mass that ends up in BBH mergers and the delay time $\tau$ between star formation and BBH merger carry information about the astrophysical processes that give rise to merging BBHs. We model the BBH merger rate in terms of the cosmic star formation history, coupled with a metallicity-dependent efficiency $\epsilon$ and a distribution of delay times $\tau$, and infer these parameters with data from the Third Gravitational-Wave Transient Catalog (GWTC-3). We find that the progenitors to merging BBHs preferentially form in low metallicity environments with a low metallicity efficiency of $\log_{10}\epsilon_{<Z_t}=-3.99^{+0.68}_{-0.87}$ and a high metallicity efficiency of $\log_{10}\epsilon_{<Z_t}=-4.60^{+0.30}_{-0.34}$ at the 90% credible level. The data also prefer short delay times. For a power-law distribution $p(\tau)\propto \tau^\alpha$, we find $\tau_\text{min}<1.9 $ Gyr and $\alpha<-1.32$ at 90% credibility. Our model allows us to extrapolate the mass density in BBHs out to high redshifts. We cumulatively integrate our modelled density rate over cosmic time to get the total mass density of merging stellar mass BBHs as a function of redshift. Today, stellar-mass BBH mergers make up only $\sim 0.01\%$ of the total stellar mass density created by high-mass ($>10\,M_\odot$) progenitors. However, because massive stars are so short-lived, there may be more mass in merging BBHs than in living massive stars as early as $\sim 2.5$ Gyr ago. We also compare to the mass in supermassive BHs, finding that the mass densities were comparable $\sim 12.5$ Gyr ago, but the mass density in SMBHs quickly increased to $\sim 75$ times the mass density in merging stellar mass BBHs by $z\sim 1$.

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