Truthful Mechanisms for Agents that Value Privacy

Citation:

Yiling Chen, Stephen Chong, Ian Kash, Tal Moran, and Salil Vadhan. Published. “Truthful Mechanisms for Agents that Value Privacy.” ACM Transactions on Economics and Computation (TEAC), 3, 4, 6/2016. TEAC Version

Abstract:

Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from truthfulness; it is not incorporated in players’ utility functions (and doing so has been shown to lead to nontruthfulness in some cases). In this work, we propose a new, general way of modeling privacy in players’ utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i. We give three mechanisms that are truthful with respect to our modeling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number n of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of n).

Last updated on 02/14/2017