BSF:2012348:The Boundaries of Privacy

BSF:2012348：隐私的边界

• 批准号: 1331343
• 负责人: Katrina Ligett
• 金额: $ 6万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331343&HistoricalAwards=false
• 关键词: BSF; 2012348; Boundaries; Privacy; BSF; 2012348; Boundaries; Privacy

This project is funded as part of the United States-Israel Collaboration in Computer Science (USICCS) program. Through this program, NSF and the United States - Israel Binational Science Foundation (BSF) jointly support collaborations among US-based researchers and Israel-based researchers. The availability of fast and cheap computers coupled with massive storage devices has enabled the collection and mining of data on a scale previously unimaginable. This opens the door to potential abuse regarding individuals' information. This work explores the fundamental tradeoffs between privacy for individuals' data and the usefulness of the information one can obtain from these large datasets.Differential privacy is a well-established paradigm aimed at mitigating the drawbacks of traditional anonymization techniques, as it provides a rigorous guarantee for the added risk to an individual in participating in a database. This research addresses fundamental questions clarifying the boundaries of what is possible under differential privacy and its relaxations, by exploring the fundamental conflicts between privacy and utility and the additional tensions introduced by computational efficiency. This work expands the potential impact of differentially private algorithms on real-world applications, and also ensures broad impact via curriculum development, pedagogical development, and outreach activities.

该项目是作为美国 - 以色列计算机科学（USICCS）计划合作的一部分资助的。通过该计划，NSF和美国 - 以色列双原则科学基金会（BSF）共同支持美国研究人员和以色列研究人员之间的合作。快速和廉价计算机的可用性以及大量存储设备的可用性使以前无法想象的比例收集和挖掘了数据。这为有关个人信息的潜在虐待打开了大门。这项工作探讨了个人数据隐私之间的基本权衡与可以从这些大型数据集中获得的信息的实用性之间的基本权衡。不同的隐私是旨在减轻传统匿名技术缺点的完善的范式，因为它为参与数据库的个人提供了严格的保证。这项研究解决了基本问题，阐明了通过探索隐私与效用之间的基本冲突以及计算效率引入的其他紧张局势，从而阐明了差异隐私及其放松下可能的边界。这项工作扩大了差异私人算法对现实世界应用的潜在影响，并通过课程开发，教学开发和外展活动确保了广泛的影响。