Or Sheffet: "Utilitarian Models of Privacy-Loss and Social Choice"

Presentation Date: 

Monday, September 29, 2014

Date: Monday, September 29, 2014
Time: 11:30am – 1:00pm
Place: Maxwell Dworkin 119

Speaker: Or Sheffet, Postdoctoral Fellow, CRCS, SEAS, Harvard University.

Title: Utilitarian Models of Privacy-Loss and Social Choice

This talk surveys two independent works. In its first part we discuss the problem of analyzing the effect of privacy concerns on the behavior of selfish and utility-maximizing agents. Previous works [GR11, Xiao13, NOS12, CCKMV13] avoid the need to provide an explicit formalization of privacy concerns by designing mechanisms that adhere to the worst-case notion of differential privacy. Our work takes the complimentary approach and is aimed at a better understanding of the behavior of agents when the privacy concerns are explicitly formalized. Specifically we characterize the behavior of selfish utility-maximizing agents in a toy-setting where agent A's incentive to discover agent B's secret type is the result of some payments between B and A.

In the second part of the talk we discuss the problem of Social Choice, where n individuals are picking together one alternative out of m possible alternatives using a social choice function -- a function that takes as input the n individuals' preferences among the alternatives and outputs a single chosen alternative, called the winner. Inspired but newly formulated ideas as to the role of clustering [BBG09], we view social choice as a proxy for maximizing social welfare.  Our premise is that agents have (possibly implicit or latent) utility functions, and the goal of a social choice function is to maximize the social welfare — i.e., (possibly weighted) sum of agent utilities — of the selected alternative. We will also discuss current, open ended, work as to maximizing utility of a matching / stable matching.

Based Joint work with Yiling Chen and Salil Vadhan (WINE'14) and Craig Boutillier, Ioannis Caragiannis, Simi Haber, Tyler Lu and Ariel Procaccia (EC' 12).

I am a fellow of Harvard's CRCS and a member of the Privacy-Tools program. Prior to my post-doc at Harvard I was a research fellow in the Simon's Institute for the Theory of Computer Science as a member of the "Theoretical Foundations of Big-Data" program. I have a B.SC in math and computer science from the Hebrew University in Jerusalem, Israel, and a M.Sc in computer science and applied math from the Weizmann Institute of Science. I have a PhD in computer science from Carnegie Mellon University, where I had the honor of being advised by prof. Avrim Blum. My research interests lie in differential privacy, algorithmic game theory, machine learning in general and clustering in particular.