research-article

Approximately optimal mechanism design via differential privacy

Authors:

Rann Smorodinsky,

Moshe TennenholtzAuthors Info & Claims

ITCS '12: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference

Pages 203 - 213

https://doi.org/10.1145/2090236.2090254

Published: 08 January 2012 Publication History

Abstract

We study the implementation challenge in an abstract interdependent values model and an arbitrary objective function. We design a generic 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 differential privacy is employed. With the complementary probability we actuate a mechanism that may be typically far from optimal but is incentive compatible. The joint mechanism inherits the desired properties from both.

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Cited By

Pourbabaee FEchenique F(2025)Binary Mechanisms under Privacy-Preserving NoiseJournal of Economic Theory10.1016/j.jet.2025.105965(105965)Online publication date: Jan-2025
https://doi.org/10.1016/j.jet.2025.105965
Huh JKandasamy KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Nash incentive-compatible online mechanism learning via weakly differentially private online learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692898(20643-20659)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692898
Li ZDing BYao LLi YXiao XZhou J(2024)Performance-Based Pricing for Federated Learning via AuctionProceedings of the VLDB Endowment10.14778/3648160.364816917:6(1269-1282)Online publication date: 1-Feb-2024
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Index Terms

Approximately optimal mechanism design via differential privacy
1. Theory of computation

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cover image ACM Conferences

ITCS '12: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference

January 2012

516 pages

ISBN:9781450311151

DOI:10.1145/2090236

Program Chair:
Shafi Goldwasser
MIT

Copyright © 2012 ACM.

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Published: 08 January 2012

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Israel Science Foundation

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ITCS '12

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SIGACT

ITCS '12: Innovations in Theoretical Computer Science

January 8 - 10, 2012

Massachusetts, Cambridge

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ITCS '12 Paper Acceptance Rate 39 of 93 submissions, 42%;

Overall Acceptance Rate 172 of 513 submissions, 34%

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Cited By

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https://doi.org/10.1016/j.jet.2025.105965
Huh JKandasamy KSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Nash incentive-compatible online mechanism learning via weakly differentially private online learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692898(20643-20659)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692898
Li ZDing BYao LLi YXiao XZhou J(2024)Performance-Based Pricing for Federated Learning via AuctionProceedings of the VLDB Endowment10.14778/3648160.364816917:6(1269-1282)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648169
Hoseinpour MHoseinpour MHaghifam MHaghifam M(2024)Privacy-Preserving and Approximately Truthful Local Electricity Markets: A Differentially Private VCG MechanismIEEE Transactions on Smart Grid10.1109/TSG.2023.330117415:2(1991-2003)Online publication date: Mar-2024
https://doi.org/10.1109/TSG.2023.3301174
Chang W(2024)A Real-time Privacy Monitoring System for Multi-tasks-based Privacy-disclosure in CrowdsourcingIEEE Access10.1109/ACCESS.2024.3513796(1-1)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3513796
Zhang LZhu TXiong PZhou WYu P(2023)A Robust Game-Theoretical Federated Learning Framework With Joint Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.314013135:4(3333-3346)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3140131
Ramakrishna RScaglione AWu TRavi NPeisert S(2023)Differential Privacy for Class-Based Data: A Practical Gaussian MechanismIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.328912818(5096-5108)Online publication date: 2023
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Eilat REliaz KMu X(2021)Bayesian privacyTheoretical Economics10.3982/TE439016:4(1557-1603)Online publication date: 2021
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Chefrour D(2021)One-Way Delay Measurement From Traditional Networks to SDNACM Computing Surveys10.1145/346616754:7(1-35)Online publication date: 18-Jul-2021
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Magrofuoco NRoselli PVanderdonckt J(2021)Two-dimensional Stroke Gesture RecognitionACM Computing Surveys10.1145/346540054:7(1-36)Online publication date: 18-Jul-2021
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