Statistically Valid Inferences from Privacy Protected Data

A Google TechTalk, presented by Gary King, 2020/09/18 Paper Title: "Statistically Valid Inferences from Privacy Protected Data" Abstract: Unprecedented quantities of data that could help social scientists understand and ameliorate the challenges of human society are presently locked away inside companies, governments, and other organizations, in part because of worries about privacy violations. We address this problem with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data and statistical validity guarantees for researchers seeking population-level insights from it. We build on the standard of ""differential privacy"" but, unlike most such approaches, we also correct for the serious statistical biases induced by privacy-preserving procedures, provide a proper accounting for statistical uncertainty, and impose minimal constraints on the choice of data analytic methods and types of quantities estimated. Our algorithm is easy to implement, simple to use, and computationally efficient; we also offer open source software to illustrate all our methods. About the Speaker: Gary King is Albert J. Weatherhead III University Professor and Director of the Institute for Quantitative Social Science. He develops and applies empirical methods in many areas of social science research, focusing on innovations that span the range from statistical theory to practical application. He has been elected to 8 honorary societies and has won more than 40 awards for his work. King has a long history of developing what are now widely used teaching and learning innovations, including as cofounder of the educational technology startups, Perusall, Learning Catalytics, and others."