Index

Lecture series are sponsored by

Prof. Prof. Michael Kearns (University of Pennsylvania)

Abstract: Differential privacy is a well-studied model for balancing the social utility of aggregated data (for instance, in medical studies or web search) with the desire for privacy by individuals. Recently it has been applied to equilibrium selection in game-theoretic settings, where it has emerged that privacy yields desirable mechanism design properties (such as truthfulness) as a by-product. We will survey these developments, and also describe an adaptation of differential privacy for “contact chaining” in social networks, a common tool in counterterrorism efforts.

Short Bio: Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, and computational social science. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is the founding director of both Penn’s Warren Center for Network and Data Sciences, and Penn’s Networked and Social Systems Engineering (NETS) undergraduate program. Kearns consults extensively in technology and finance, and is a Fellow of the American Academy of Arts and Science, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.