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Differential Privacy as a Tool for Mechanism Design in Large Systems

Published: 08 December 2014 Publication History

Abstract

In this talk we overview how differential privacy gives a collection of tools that can be easily applied to design algorithms which enjoy remarkable incentive properties in large systems and markets -- settings in which the number of interacting agents is large, and each of the individual players are "small". We illustrate this power with two vignettes -- designing mediators to coordinate equilibrium behavior in games of incomplete information (due to Kearns, Pai, Roth, and Ullman, and Rogers and Roth, 2014), and designing ascending price auctions such that sincere bidding is an asymptotic dominant strategy (due to Huang, Hsu, Roth, Roughgarden, and Wu, 2014).
In both of these settings, we get good incentive properties under the assumption that the market is "large" in some sense. However, we discuss how our methodology (via differential privacy) allows us to make substantially milder "large market" assumptions than those commonly appearing in the literature.
  1. Differential Privacy as a Tool for Mechanism Design in Large Systems

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    Published In

    cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 42, Issue 3
    December 2014
    80 pages
    ISSN:0163-5999
    DOI:10.1145/2695533
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 December 2014
    Published in SIGMETRICS Volume 42, Issue 3

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