IDEAS home Printed from https://ideas.repec.org/a/aea/aecrev/v104y2014i5p431-35.html
   My bibliography  Save this article

Mechanism Design in Large Games: Incentives and Privacy

Author

Listed:
  • Michael Kearns
  • Mallesh M. Pai
  • Aaron Roth
  • Jonathan Ullman

Abstract

We study the design of mechanisms satisfying a novel desideratum: privacy. This requires the mechanism not reveal "much" about any agent's type to other agents. We propose the notion of joint differential privacy: a variant of differential privacy used in the privacy literature. We show by construction that mechanisms satisfying our desiderata exist when there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our results imply that in large economies, privacy concerns of agents can be accommodated at no additional "cost" to standard incentive concerns.

Suggested Citation

  • Michael Kearns & Mallesh M. Pai & Aaron Roth & Jonathan Ullman, 2014. "Mechanism Design in Large Games: Incentives and Privacy," American Economic Review, American Economic Association, vol. 104(5), pages 431-435, May.
    • Handle: RePEc:aea:aecrev:v:104:y:2014:i:5:p:431-35
    Note: DOI: 10.1257/aer.104.5.431
    as

    Download full text from publisher

    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.5.431
    Download Restriction: no
    File URL: http://www.aeaweb.org/aer/ds/10405/P2014_1095_ds.zip
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ronen Gradwohl & Moshe Tennenholtz, 2020. "Coopetition Against an Amazon," Papers 2005.10038, arXiv.org, revised Nov 2021.
    2. Kobbi Nissim & Rann Smorodinsky & Moshe Tennenholtz, 2018. "Segmentation, Incentives, and Privacy," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1252-1268, November.
    3. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    4. Emily Diana & Michael Kearns & Seth Neel & Aaron Roth, 2019. "Optimal, Truthful, and Private Securities Lending," Papers 1912.06202, arXiv.org.
    5. Ran Eilat & Kfir Eliaz Eliaz & Xiaosheng Mu, 2021. "Bayesian Privacy," Working Papers 2021-65, Princeton University. Economics Department..
    6. Eliaz, Kfir & Eilat, Ran & Mu, Xiaosheng, 2019. "Optimal Privacy-Constrained Mechanisms," CEPR Discussion Papers 13536, C.E.P.R. Discussion Papers.

    More about this item

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:104:y:2014:i:5:p:431-35. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.