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BIGDATA: F: DKA: Scalable, Private Algorithms for Continual Data Analysis

BIGDATA：F：DKA：用于持续数据分析的可扩展、私有算法

基本信息

批准号：
1832766
负责人：
Adam Smith
金额：
$ 2.15万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-24 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832766&HistoricalAwards=false
关键词：
BIGDATA DKA Scalable Private Algorithms

项目摘要

For the very same reasons that big data is transforming modern life, it also presents a profound threat to privacy and the control of personal information. A major challenge associated with big data is to enable statistical analysis of complex data sets, without compromising the privacy of the individuals whose data they contain. Addressing this challenge is both necessary, since access to many data sources is restricted due to privacy concerns, and difficult, as numerous attacks on supposedly anonymized data demonstrate. This project will investigate the design and limitations of algorithms for the private, continual analysis of time-varying data sets. That is, it will study algorithms that release information about a data set as it is collected (say, in the form of a data stream from the web, or a long-term sociological study). The research will advance the state of the art in the private analysis of "big" -- massive, complex, time-varying -- data. If successful, the project will provide enabling technologies that facilitate research in areas where access to sensitive data is limited by confidentiality concerns.The project will focus on the design of algorithms that satisfy differential privacy -- a rigorous notion of privacy that is widely studied in computer science and related fields. The privacy implications of sequential releases are still poorly understood, and relatively few of the algorithms developed in the extensive recent literature on private data analysis allow for sequential releases with high accuracy. The two major thrusts of the project are (1) algorithms for the "continual release" model, and (2) algorithms for the "local" model, which offers even stronger privacy guarantees. The work will provide novel algorithmic design techniques and understanding of complexity-theoretic limitations of algorithms for these models. The research will entail advances in related areas such as learning theory, statistical inference and streaming algorithms. The project will also include educational, outreach and work-force training activities designed to broaden the impact of the research.For further information see the project web site at: http://www.cse.psu.edu/~asmith/projects/continual/

出于同样的原因，大数据正在改变现代生活，它也对隐私和个人信息的控制构成了深刻的威胁。与大数据相关的一个主要挑战是对复杂数据集进行统计分析，而不损害数据所包含的个人的隐私。应对这一挑战既有必要，因为由于隐私问题，对许多数据源的访问受到限制，也很困难，因为对所谓的匿名数据的许多攻击表明。这个项目将研究算法的设计和局限性，用于对时变数据集进行私有的、连续的分析。也就是说，它将研究在收集数据集时发布有关数据集的信息的算法（例如，以网络数据流的形式，或长期社会学研究）。这项研究将推动私人分析“大”-大量、复杂、时变-数据的最新技术。如果成功，该项目将提供有利的技术，促进在因保密问题而限制获取敏感数据的领域进行研究，该项目将侧重于设计满足差别隐私的算法，差别隐私是计算机科学和相关领域广泛研究的严格的隐私概念。顺序发布的隐私影响仍然知之甚少，相对较少的算法，在最近的大量文献中开发的私人数据分析允许顺序发布的高准确性。该项目的两个主要目标是（1）“持续发布”模型的算法，以及（2）“本地”模型的算法，这提供了更强的隐私保证。这项工作将提供新的算法设计技术和理解这些模型的算法的复杂性理论的局限性。这项研究将带来相关领域的进展，如学习理论，统计推断和流算法。该项目还将包括旨在扩大研究影响的教育、推广和劳动力培训活动。欲了解更多信息，请访问项目网站：http://www.cse.psu.edu/~asmith/projects/continual/

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Algorithmic Stability for Adaptive Data Analysis

自适应数据分析的算法稳定性

DOI：
10.1137/16m1103646
发表时间：
2021
期刊：
SIAM Journal on Computing
影响因子：
1.6
作者：
Bassily, Raef;Nissim, Kobbi;Smith, Adam;Steinke, Thomas;Stemmer, Uri;Ullman, Jonathan
通讯作者：
Ullman, Jonathan

When is non-trivial estimation possible for graphons and stochastic block models?‡

什么时候可以对图子和随机块模型进行非平凡的估计？