Improving Data Techniques with Gary King of Harvard University
Statistically Valid Inferences from Privacy Protected Data
Venerable procedures for privacy protection and data sharing within academia, companies, and governments, and between sectors have been proven to be completely inadequate (e.g., respondents in de-identified surveys can usually be re-identified). At the same time, 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 these problems with a general-purpose data access and analysis system with mathematical guarantees of privacy for individuals who may be represented in the data, statistical guarantees for researchers seeking insights from it, and protection for society from some fallacious scientific conclusions. 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.
Speaker Bio: Gary King is the Albert J. Weatherhead III University Professor at Harvard University – one of 25 with Harvard's most distinguished faculty title – and Director of the Institute for Quantitative Social Science. Prof. King develops and applies empirical methods in many areas of social science, focusing on innovations that span the range from statistical theory to practical application. Prof. King is a proud graduate of SUNY New Paltz (B.A., 1980) and the University of Wisconsin-Madison (M.A., Ph.D., 1984). He taught at NYU for three years before coming to Harvard in 1987.RSVP