CDI-Type II: Collaborative Research: Integrating Statistical and Computational Approaches to Privacy

CDI-类型 II：协作研究：整合隐私统计和计算方法

• 批准号: 0941518
• 负责人: Stephen Fienberg
• 金额: $ 61.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941518&HistoricalAwards=false
• 关键词: CDI; Type; II; Collaborative; Research

Data privacy is a fundamental problem of the modern information infrastructure. Increasing volumes of personal and sensitive data are collected and archived by health networks, government agencies, search engines, social networking websites, and other organizations. The social benefits of analyzing these databases are significant. At the same time, the release of information from sensitive data repositories can be devastating to the privacy of individuals and organizations. The challenge is to discover and release important characteristics of these databases without compromising the privacy of those whose data they contain. The main goal of this project is to design scalable computational techniques that are statistically sound, yield broadly useful data, and yet preserve privacy in the face of realistic external information. The project aims to integrate two essentially different approaches to the complex problem of data privacy. The reconciliation of these approaches raises a number of fundamental questions for statistical theory and cryptography, as well as methodological challenges that must be overcome to enable practical applications. This research is centered around three themes: (1) Integrating the computationally-focused, rigorous definitions of privacy emanating from computer science with notions of utility from statistics. (2) Developing cryptographic protocols for distributing privacy-preserving algorithms for valid statistical analysis among a group of servers so as to avoid pooling data in any single location. (3) Understanding the practical potential of the developed techniques by applying them to concrete problems in the behavioral and social sciences and analyzing important data sources from the official statistical community. The research will be carried out in collaboration with social scientists and industry researchers. The project will increase awareness of data privacy issues and promote research on statistical disclosure limitation, cryptography and privacy-preserving data mining. Moreover, this research will transform the way statistical agencies, social scientists, medical researchers, and those in industry approach privacy?in particular, how they collect, share and publish information. The integration of statistical and cryptographic methods in the form of ex ante provably secure procedures will provide the essential scientific fundamentals for official statistical agencies to fulfill their mission of useful data production, which the proliferation of digital information has endangered. Finally, the new techniques will permit opening the vault of industrial data, such as search logs and data on social networks, to statistical analysis?greatly expanding the research domain of the social and health sciences.

数据隐私是现代信息基础设施的一个基本问题。越来越多的个人和敏感数据被卫生网络、政府机构、搜索引擎、社交网站和其他组织收集和存档。分析这些数据库的社会效益是显著的。与此同时，从敏感数据存储库中发布信息可能对个人和组织的隐私造成破坏。面临的挑战是发现和发布这些数据库的重要特征，而不损害数据所包含的数据的隐私。该项目的主要目标是设计可扩展的计算技术，这些技术在统计上是合理的，产生广泛有用的数据，并且在面对现实的外部信息时保护隐私。该项目旨在整合两种本质上不同的方法来解决复杂的数据隐私问题。这些方法的调和为统计理论和密码学提出了一些基本问题，以及必须克服以实现实际应用的方法学挑战。本研究主要围绕三个主题展开：（1）将计算机科学中以计算为中心的严格的隐私定义与统计学中的效用概念相结合。(2)开发加密协议，用于在一组服务器之间分发隐私保护算法，以进行有效的统计分析，从而避免在任何单个位置汇集数据。(3)通过将其应用于行为和社会科学的具体问题，并分析来自官方统计界的重要数据源，了解所开发技术的实际潜力。这项研究将与社会科学家和行业研究人员合作进行。该项目将提高对数据隐私问题的认识，促进对统计披露限制、密码学和保护隐私的数据挖掘的研究。此外，这项研究将改变统计机构、社会科学家、医学研究人员和行业人士处理隐私的方式？特别是它们如何收集、分享和公布信息。以事先可证明安全的程序的形式将统计和密码方法结合起来，将为官方统计机构提供必要的科学基础，以履行其产生有用数据的使命，而数字信息的扩散已危及这一使命。最后，新技术将允许开放工业数据库，如搜索日志和社交网络上的数据，以进行统计分析。大大扩展了社会科学和健康科学的研究领域。