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Approximately Optimal Mechanism Design via Differential Privacy ∗ (1004)
| Citations: | 37 - 1 self |
BibTeX
@MISC{Nissim04approximatelyoptimal,
author = {Kobbi Nissim and Rann Smorodinsky and Moshe Tennenholtz and James Schummer and Roberto Serrano},
title = {Approximately Optimal Mechanism Design via Differential Privacy ∗},
year = {1004}
}
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Abstract
In this paper we study the implementation challenge in an abstract interdependent values model and an arbitrary objective function. We design a mechanism that allows for approximate optimal implementation of insensitive objective functions in ex-post Nash equilibrium. If, furthermore, values are private then the same mechanism is strategy proof. We cast our results onto two specific models: pricing and facility location. The mechanism we design is optimal up to an additive factor of the order of magnitude of one over the square root of the number of agents and involves no utility transfers. Underlying our mechanism is a lottery between two auxiliary mechanisms — with high probability we actuate a mechanism that reduces players influence on the choice of the social alternative, while choosing the optimal outcome with high probability. This is where the recent notion of differential privacy is employed. With the complementary probability we actuate a mechanism that is typically far from optimal but is incentive compatible. The joint mechanism inherits the desired properties from both. We thank Amos Fiat and Haim Kaplan for discussions at an early stage of this research. We thank Frank McSherry and Kunal Talwar for helping to clarify issues related to the constructions in [22]. Finally, we thank Jason Hartline,
Keyphrases
differential privacy optimal mechanism design high probability strategy proof haim kaplan player influence optimal outcome approximate optimal implementation facility location kunal talwar recent notion specific model social alternative early stage ex-post nash equilibrium desired property additive factor auxiliary mechanism joint mechanism utility transfer arbitrary objective function frank mcsherry square root complementary probability jason hartline implementation challenge abstract interdependent value model insensitive objective function amos fiat
