TWC: Medium: Distributed Differential Privacy

TWC：媒介：分布式差异隐私

• 批准号: 1513694
• 负责人: AARON ROTH
• 金额: $ 120万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513694&HistoricalAwards=false
• 关键词: TWC; Medium; Distributed; Differential; Privacy

When data is controlled by diverse stake-holders, privacy concerns may limit sharing. Loose federation can be an obstacle to dependability because lack of a global view can limit large-scale coordination and stability. Lack of a global view is one of the problems that contributed to the 2008 financial collapse. Ideally, an outside party could coordinate behavior, but this is often impossible: domains may be reluctant to share their local information. Differential privacy can mitigate these concerns -- it provides a strong, attractive privacy guarantee that protects data owners from risks associated disclosure of their data, but this guarantee can be hard to achieve in distributed settings. This project is developing practical tools for distributed private data analysis that can be used by non-experts in a variety of applications, allowing for important new analyses of distributed data. The project also includes substantial outreach activities, in the form of course development and workshop organization, and will train PhD students to be future leaders in the development of privacy technologies. This project advances the practical theory of differential privacy in distributed settings. It has three main thrusts: 1) to extend the theory of differential privacy to work with relaxed guarantees, amenable to high-accuracy analyses when there are only a relatively small number of parties with limited ability to collude; 2) to develop programming languages and automatic verification tools capable of automatically certifying the differential privacy properties of distributed systems, in which each party has only partial access to the data; and 3) to develop tool chains to implement differentially private algorithms in distributed settings, using (among other technologies) secure multi-party computation as a computational substrate.

当数据由不同的数据持有者控制时，隐私问题可能会限制共享。 松散的联盟可能会成为可靠性的障碍，因为缺乏全局视图会限制大规模的协调和稳定性。缺乏全球视野是导致2008年金融崩溃的问题之一。理想情况下，外部方可以协调行为，但这通常是不可能的：域可能不愿意共享其本地信息。 差异隐私可以缓解这些担忧-它提供了一个强大的，有吸引力的隐私保证，保护数据所有者免受与其数据泄露相关的风险，但这种保证在分布式环境中很难实现。 该项目正在开发实用的分布式私人数据分析工具，非专家可以在各种应用程序中使用这些工具，从而可以对分布式数据进行重要的新分析。该项目还包括以课程开发和研讨会组织形式开展的大量外展活动，并将培养博士生成为隐私技术开发的未来领导者。该项目提出了分布式环境下的差异隐私的实用理论。它有三个主要目标：1）将差分隐私理论扩展到宽松的保证，当只有相对较少的参与方具有有限的共谋能力时，可以进行高精度分析; 2）开发能够自动证明分布式系统的差分隐私属性的编程语言和自动验证工具，其中每一方只能部分访问数据;以及3）开发工具链以在分布式设置中实现差异私有算法，使用（在其他技术中）安全多方计算作为计算基底。