Adam Smith

Penn State University


Consider an agency holding a large database of sensitive personal information -- medical records, census survey answers, or web search records, 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 simply "database privacy".


In this talk, I will describe "differential privacy", a notion which emerged from a recent line of work in theoretical computer science that seeks to formulate and satisfy rigorous definitions of privacy for such statistical databases. I will sketch some basic techniques for achieving differential privacy as well as some recent results.



Adam Smith is an associate professor in the Department of Computer Science and Engineering at Penn State. 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).


B17 Upson Hall

Thursday, September 16, 2010

Refreshments at 3:45pm in the Upson 4th Floor Atrium


Computer Science


Fall 2010

Rigorous Foundations for privacy

in statistical databases