CSIS Seminar

Data Privacy and Statistical Inference

Abstract

Consider an agency holding a large database of sensitive personal information—say, medical records, census survey answers, web searches, or genetic data. The agency would like to discover and publicly release global characteristics of the data while protecting the privacy of individuals' records. I will begin by discussing what makes this problem difficult, illustrating some challenges via recent attacks. Motivated by this, I will present differential privacy, a rigorous definition of privacy in statistical databases that is now widely studied, and increasingly used to analyze and design deployed systems. I’ll conclude by explaining recent research on statistical inference from differentials private outputs, along the way discussing some of the challenges and promises of deployments in government and industry.

Speaker Bio

Adam Smith is a professor of computer science at Boston University. From 2007 to 2017, he served on the faculty of the Computer Science and Engineering Department at Penn State. His research interests lie in data privacy and cryptography, and their connections to machine learning, statistics, information theory, and quantum computing. He obtained his Ph.D. from MIT in 2004 and has held postdoc and visiting positions at the Weizmann Institute of Science, UCLA, Boston University and Harvard. He received a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2009; a Theory of Cryptography Test of Time award in 2016; and the 2017 Gödel Prize. These last two awards were joint with C. Dwork, F. McSherry, and K. Nissim.