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Computer Science Department Colloquium
4/29/2014 11:00 am
CoRE A(Room 301)

Pinning Down "Privacy" in Statistical Databases

Adam Smith, Pennsylvania State University

Faculty Host: Rebecca Wright

Abstract

Consider an agency holding a large database of sensitive personal information -- medical records, census survey answers, web search records, or genetic data, for example. The agency would like to discover and publicly release global characteristics of the data (say, to inform policy and business decisions) while protecting the privacy of individuals' records. This problem is known variously as "statistical disclosure control", "privacy-preserving data mining" or "private data analysis". We will begin by discussing what makes this problem difficult, and exhibit some of the problems that plague simple attempts at anonymization. Motivated by this, we will discuss differential privacy, a rigorous definition of privacy in statistical databases that has received significant recent attention. We will survey some basic techniques for designing differentially private algorithms and conclude by laying out some major challenges facing researchers in this area.

Bio

Adam Smith is an associate professor in the Department of Computer Science and Engineering at Penn State, currently on sabbatical at Boston University. His research interests lie in cryptography, privacy and their connections to information theory, quantum computing and statistics. He received his Ph.D. from MIT in 2004 and was subsequently a visiting scholar at the Weizmann Institute of Science and UCLA. In 2009, he received a Presidential Early Career Award for Scientists and Engineers (PECASE).