TC: Small: Adaptive Differentially Private Data Release

TC：小型：自适应差分隐私数据发布

• 批准号: 1117763
• 负责人: Li Xiong
• 金额: $ 41.36万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117763&HistoricalAwards=false
• 关键词: TC; Small; Adaptive; Differentially; Private

Current information technology enables many organizations to collect, store, and use massive amount and various types of information about individuals. Governments and other organizations increasingly recognize the critical value and enormous opportunities in sharing such a wealth of information. However data privacy has been a major barrier for such information sharing, bringing much attention to privacy preserving data publishing and analysis techniques. Differential privacy is widely accepted as one of the strongest unconditional privacy guarantees. While many effective mechanisms have been proposed for the interactive model with differential privacy, non-interactive data release with differential privacy remains an open problem with the recent years only see negative results. This project aims to build a data-driven and adaptive framework for differentially private data release. It circumvents the hardness of differentially private data release in the non-interactive setting by novel and sophisticated use of the differentially private primitives exploiting the characteristics of the underlying data. The specific research objectives include: (1) design adaptive query strategies for releasing data with differential privacy, including traditional relational data and high dimensional and sparse set-valued data, (2) design statistical inference technique to accurately answer user queries using the released data, and (3) design algorithms to model and incorporate potentially dynamic workload characteristics in the framework. In addition to formal analysis and experimental evaluations, the project will evaluate and integrate the developed solutions in real health applications at Emory University to support health research while providing rigorous privacy guarantee.Success of the proposed research will help overcoming barriers for large scale data sharing and will have broader impacts to large societies beyond the field of data privacy and information management. The proposal also includes a set of closely integrated educational activities including new course development on data privacy and security emphasizing a strong interdisciplinary aspect, continued involvement of undergraduate students in research, and encouragement of women and minority for participation. The project also closely aligns with Emory's university-wide strategic initiatives in Predictive Health and will help develop the new Ph.D. program in Computer Science and Informatics at Emory University.

当前的信息技术使许多组织能够收集、存储和使用关于个人的大量和各种类型的信息。各国政府和其他组织日益认识到分享这种丰富信息的关键价值和巨大机会。然而，数据隐私一直是此类信息共享的主要障碍，引起了人们对保护隐私的数据发布和分析技术的关注。差别隐私被广泛认为是最有力的无条件隐私保障之一。虽然对差分隐私的交互模型提出了许多有效的机制，但差分隐私的非交互式数据发布仍然是一个悬而未决的问题，近年来只看到负面的结果。本项目旨在构建一个数据驱动和自适应的框架，用于差异化私有数据发布。它通过新颖而复杂地使用差分私有原语来利用底层数据的特征，从而避免了在非交互环境中发布差分私有数据的困难。具体的研究目标包括：(1)设计自适应查询策略，用于发布具有差异隐私的数据，包括传统的关系数据和高维和稀疏集值数据；(2)设计统计推断技术，利用发布的数据准确回答用户的查询；(3)设计算法，在框架中建模并纳入潜在的动态工作负载特征。除了正式分析和实验评估外，该项目还将评估和整合开发的解决方案在埃默里大学的实际健康应用中，以支持健康研究，同时提供严格的隐私保障。该研究的成功将有助于克服大规模数据共享的障碍，并将对数据隐私和信息管理领域以外的大型社会产生更广泛的影响。该建议还包括一系列紧密结合的教育活动，包括开发有关数据隐私和安全的新课程，强调跨学科的方面，继续让本科生参与研究，以及鼓励妇女和少数民族参与。该项目还与埃默里大学在预测性健康方面的全校战略举措密切相关，并将帮助埃默里大学开发新的计算机科学与信息学博士课程。