TWC: Frontier: Privacy Tools for Sharing Research Data

信息技术、统计计算的进步以及通过互联网获得的海量数据正在改变计算社会科学。然而，一个主要的挑战是维护人类受试者的隐私。该项目是一个广泛的、多学科的努力，旨在帮助收集、分析和共享敏感数据，同时为个别主题提供隐私。调查人员将计算机科学、社会科学、统计学和法学结合在一起，寻求完善和制定隐私和数据效用的定义和衡量标准，并设计一系列处理敏感数据的技术、法律和政策工具。除了对世界各地的研究基础设施做出贡献外，该项目中提出的想法将使社会更广泛地受益，因为它正在努力解决包括公共卫生和电子商务在内的许多其他领域的数据隐私问题。这个项目将从数学和法律两个方面定义和衡量隐私，并探索可能更普遍或更实用的隐私的替代定义。该项目将研究差异隐私的变体，并开发新的理论结果，用于目前不合适或不切实际的环境中。这项研究将使人们更好地了解用于分析和共享隐私敏感数据的各种算法的实际性能和可用性。该项目将开发这些算法和法律工具的安全实现，这些算法和法律工具将公开提供，并用于使哈佛定量社会科学研究所的数据平均网络能够更广泛地访问隐私敏感数据集。

Information technology, advances in statistical computing, and the deluge of data available through the Internet are transforming computational social science. However, a major challenge is maintaining the privacy of human subjects. This project is a broad, multidisciplinary effort to help enable the collection, analysis, and sharing of sensitive data while providing privacy for individual subjects. Bringing together computer science, social science, statistics, and law, the investigators seek to refine and develop definitions and measures of privacy and data utility, and design an array of technological, legal, and policy tools for dealing with sensitive data. In addition to contributing to research infrastructure around the world, the ideas developed in this project will benefit society more broadly as it grapples with data privacy issues in many other domains, including public health and electronic commerce. This project will define and measure privacy in both mathematical and legal terms, and explore alternate definitions of privacy that may be more general or more practical. The project will study variants of differential privacy and develop new theoretical results for use in contexts where it is currently inappropriate or impractical. The research will provide a better understanding of the practical performance and usability of a variety of algorithms for analyzing and sharing privacy-sensitive data. The project will develop secure implementations of these algorithms and legal instruments, which will be made publicly available and used to enable wider access to privacy-sensitive data sets at the Harvard Institute for Quantitative Social Science's Dataverse Network.