TC: Large: Collaborative Research: Practical Privacy: Metrics and Methods for Protecting Record-level and Relational Data

TC：大型：协作研究：实用隐私：保护记录级和关系数据的指标和方法

• 批准号: 1012593
• 负责人: John Abowd
• 金额: $ 132.67万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1012593&HistoricalAwards=false
• 关键词: TC; Large; Collaborative; Research; Practical

Safely managing the release of data containing confidential information about individuals is a problem of great societal importance. Governments, institutions, and researchers collect data whose release can have enormous benefits to society by influencing public policy or advancing scientific knowledge. But dissemination of these data can only happen if the privacy of the respondents' data is preserved or if the amount of disclosure is limited.The goal of this research project is to bridge the gap between the statistics and computer science community and between theory and practice in limiting disclosure. The research focuses on limiting statistical disclosure using synthetic data, the most advanced method from the statistics community that enables the construction of public data sets with strong statistical properties; the research incorporates formal privacy guarantees from the computer science community into this approach. Techniques focus on household data and relational data dealing with real problems motivated by the U.S. Census Bureau and related agencies.The approach of the team is based on the development of novel techniques for boosting the utility of synthetic data generation methods with formal privacy guarantees; novel formal privacy models that formalize attackers implicitly considered in the statistics literature, and new attacker models that allow an exploration of the space between weak and strong adversaries; and novel techniques designed for data from censuses or surveys about households which have a relational structure.The research has broad impact by influencing the methodology of statistical agencies around the world. The project also develops a open-source toolkit for limiting disclosure in data publishing with formal privacy guarantees; it integrates undergraduate students into research, and it creates educational material for material for practitioners responsible for safe data handling.For further information see the project web site at the URL:www.cs.cornell.edu/bigreddata/privacy

安全管理包含个人机密信息的数据的发布是一个具有重大社会重要性的问题。政府、机构和研究人员收集的数据，其发布可以通过影响公共政策或推进科学知识，为社会带来巨大利益。但是，这些数据的传播只有在被调查者的数据隐私得到保护或披露的数量有限的情况下才能发生。这个研究项目的目标是弥合统计学和计算机科学界之间的差距，以及限制信息披露的理论和实践之间的差距。该研究侧重于使用合成数据限制统计披露，这是统计界最先进的方法，可以构建具有强大统计属性的公共数据集；该研究将计算机科学界的正式隐私保证纳入了这种方法。技术侧重于处理由美国人口普查局和相关机构引发的实际问题的家庭数据和关系数据。该团队的方法是基于新技术的开发，以提高具有正式隐私保证的合成数据生成方法的实用性；新的正式隐私模型，将统计文献中隐含的攻击者形式化，以及新的攻击者模型，允许探索弱对手和强对手之间的空间；以及为具有关系结构的家庭的人口普查或调查数据设计的新技术。该研究对世界各国统计机构的方法产生了广泛的影响。该项目还开发了一个开源工具包，用于限制数据发布中的披露，并提供正式的隐私保证；它将本科生纳入研究，并为负责安全数据处理的从业人员创建教材。欲了解更多信息，请参阅项目网站的URL:www.cs.cornell.edu/bigreddata/privacy