Truthful Mechanisms for Agents that Value Privacy


Yiling Chen, Stephen Chong, Ian A. Kash, Tal Moran, and Salil P. Vadhan. 2011. “Truthful Mechanisms for Agents that Value Privacy.” CoRR, abs/1111.5472. ArXiv Version


Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from the truthfulness; it is not incorporated in players' utility functions (and doing so has been shown to lead to non-truthfulness in some cases). In this work, we propose a new, general way of modelling 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 small privacy cost to player $i$. We give three mechanisms that are truthful with respect to our modelling 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 $n$).

Acknowledgements: This paper was supported, in part, by Harvard's Center for Research on Computation and Society, Google Inc., Microsoft Research Silicon Valley, and Stanford University.
Last updated on 04/13/2019