Aug 19 – 23
Santa Barbara






Tuesday, August 21, 2012    15:15-16:30

Tutorial — Pinning Down "Privacy" in Statistical Databases
Adam Smith, Pennsylvania State University


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 privacyof individuals' records. This problem is known variously as "statistical disclosure control", "privacy-preserving data mining" or "private data analysis".

This tutorial will describe "differential privacy", a notion which emerged from a recent line of work that seeks to formulate and satisfy rigorous definitions of privacy for such statistical databases. We will begin by discussing "cryptanalysis" for data privacy (deanonymization attacks). Motivated by this, we will discuss differential privacy and related definitions, and then explore techniques for designing differentially private algorithms.

Speaker Bio:

Adam Smith is an associate professor in the Department of Computer Science and Engineering at the Pennsylvania State University. His research interests lie in cryptography and data 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. He is the recipient of an NSF CAREER award and a Presidential Early Career Award for scientists and Engineers (PECASE).