Adam Smith
Pennsylvania State University
Computer Science and Engineering
Computer Science Building, Room 151
Faculty Host: Gerome Miklau
Consider an agency holding a large database of sensitive personal information (perhaps medical records, census survey answers, or web search records). 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.