Pinning Down “Privacy” in Statistical Databases: Adam Smith, Penn State
Wednesday, November 13, 2013
Location: Hariri Institute
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. Finally, we survey some basic techniques for designing differentially private algorithms.
This introductory talk complements a day of talks on data privacy research to be held at BU on Friday, November 15:
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).