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.
This project is funded by a National Science Foundation Secure and Trustworthy Cyberspace Frontier Grant and a gift from Google. For more information, see the original proposed project description to NSF (2012).