Seminar Title: Privacy and Statistical Inference: An Information-theoretic Approach

Speaker and Affiliation: Lalitha Sankar, Department of Electrical, Computer, and Energy Engineering at Arizona State University

Abstract:  As information about individuals and enterprises moves to an entirely digital medium, keeping certain aspects of the data confidential (even) from the legitimate data users, i.e., the problem of information privacy, is becoming an important and immediate societal problem. While the benefits (utility) of electronic data are multi-fold, there is a need to provide precise guarantees and limits on the private data leaked and quantify the tradeoff between utility and privacy. In this talk, we introduce an information-theoretic (IT) framework to formulate and study the privacy-utility tradeoff (PUT) problem and illustrate the effect of randomizing privacy mechanisms designed to minimize mutual information based leakage measure for a desired utility. We then focus on a specific problem of publishing datasets with privacy guarantees for the statistical inference problem of binary hypothesis testing under the Neyman-Pearson formulation. We present a PUT problem using mutual information as the privacy metric and the relative entropy between the two distributions of the output (postrandomization) source classes as the utility metric. For the high privacy regime, we present a Euclidean information-theoretic (E-IT) approximation to the tradeoff problem and show that the solution to the E-IT approximation is independent of the alphabet size and preserves the privacy of the source symbols in inverse proportion to their likelihood. Time permitting we may discuss comparisons of IT-based privacy metrics with others such as differential privacy.

A part of the presentation is based on joint work with Jiachun Liao, Vincent Tan, and Flavio du Pin Calmon.

Bio:  Lalitha Sankar received the B.Tech degree from the Indian Institute of Technology, Bombay, the M.S. degree from the University of Maryland, and the Ph.D degree from Rutgers University in 2007. She is presently an Assistant Professor in the ECEE department at Arizona State University. Prior to this, she was an Associate Research Scholar at Princeton University. Following her doctorate, Dr Sankar was a recipient of a three year Science and Technology Teaching Postdoctoral Fellowship from the Council on Science and Technology at Princeton University. Prior to her doctoral studies, she was a Senior Member of Technical Staff at AT&T Shannon Laboratories. Her research interests include information privacy and cyber-security in distributed and cyber-physical systems, network information theory and its applications to model and study large data systems. She received the NSF CAREER award in 2014. She received the IEEE Globecom 2011 Best Paper Award for her work on privacy of side-information in multi-user data systems. For her doctoral work, she received the 2007-2008 Electrical Engineering Academic Achievement Award from Rutgers University.