TWC SBES: Medium: Utility for Private Data Sharing in Social Science

科学进步的关键之一是共享研究数据。当数据包含有关人类受试者的信息时，不分享数据的动机就会更强。最大的担忧是关于个人的隐私特定信息必须在任何时候都受到保护。最近在隐私的数学概念方面取得的进展让人们燃起了希望，即这些数据可以得到适当的清理，并分发给其他研究小组，而不会泄露任何个人的信息。为了使这项工作物有所值，经过消毒的数据必须对统计分析有用。该项目解决了使经过消毒的数据变得有用的研究挑战。该项目的第一部分涉及算法的设计，这些算法产生受隐私限制的有用的经过消毒的数据。该项目的第二部分涉及开发经过消毒的数据的统计分析工具。现有的统计例程不是为在经过消毒的数据中发现的复杂噪声模式类型而设计的；它们的天真使用往往会导致遗漏发现或错误地声称具有统计意义。该项目的目标应用是具有地理特征的社会科学数据集。这一提议的智力价值是为净化数据的算法和用于分析数据的统计工具发展了一种效用理论。更广泛的影响是，研究小组分享有用但保护隐私的研究数据的能力有所提高。

One of the keys to scientific progress is the sharing of research data. When the data contain information about human subjects, the incentives not to share data are stronger. The biggest concern is privacy - specific information about individuals must be protected at all times. Recent advances in mathematical notions of privacy have raised the hope that the data can be properly sanitized and distributed to other research groups without revealing information about any individual. In order to make this effort worthwhile, the sanitized data must be useful for statistical analysis. This project addresses the research challenges in making the sanitized data useful. The first part of the project deals with the design of algorithms that produce useful sanitized data subject to privacy constraints. The second part of the project deals with the development of tools for the statistical analysis of sanitized data. Existing statistical routines are not designed for the types of complex noise patterns that are found in sanitized data; their naive use will often result in missed discoveries or false claims of statistical significance. The target application for this project is a social science dataset with geographic characteristics. The intellectual merit of this proposal is the development of a utility theory for algorithms that sanitize data and statistical tools for their analysis. The broader impact is the improved ability of research groups to share useful, but privacy-preserving, research data.