Truthful mechanisms for agents that value privacy

  • Authors:

  • Yiling Chen;Stephen Chong;Ian A. Kash;Tal Moran;Salil Vadhan

  • Affiliations:

  • Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA;Microsoft Research Cambridge, Cambridge, United Kingdom;IDC Herzliya, Herzliya, Israel;Harvard University, Cambridge, MA, USA

  • Venue:
  • Proceedings of the fourteenth ACM conference on Electronic commerce
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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).