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: 1012141
• 负责人: Jerome Reiter
• 金额: $ 58.32万
• 依托单位: Duke 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 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1012141&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

安全地管理包含关于个人的机密信息的数据的发布是具有重大社会重要性的问题。政府、机构和研究人员收集数据，这些数据的发布可以通过影响公共政策或推进科学知识而给社会带来巨大利益。但是，只有在保护受访者数据的隐私或限制披露量的情况下，这些数据才能传播。本研究项目的目标是弥合统计学和计算机科学界之间以及限制披露的理论和实践之间的差距。该研究的重点是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特性的公共数据集;该研究将计算机科学界的正式隐私保证纳入这种方法。 技术重点是家庭数据和关系数据处理真实的问题的动机由美国人口普查局和相关机构。该小组的方法是基于新技术的发展，以促进具有正式隐私保障的合成数据生成方法的效用;新的正式隐私模型，正式的攻击者隐式考虑在统计文献，新的攻击者模型可以探索强弱对手之间的空间;以及针对具有关系结构的家庭人口普查或调查数据设计的新技术。该研究通过影响世界各地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放源代码工具包，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育材料。www.cs.cornell.edu/bigreddata/privacy