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Guest Seminar: Anand Sarwate
May 20 @ 2:00 pm - 3:30 pm
Tuesday 20 May 2.00pm UK time
University of Bristol, School of Mathematics, Fry Building/ Online
Anand Sarwate
Rutgers, The State University of New Jersey.
‘An information theorist visits differential privacy’
Abstract: Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be understood in terms of properties of this channel. In this talk I will discuss some of the key touchpoints between information theory and the formulation/application of differential privacy.
About the speaker: Anand D. Sarwate is currently an Associate Professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in mathematics and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security.