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Optimal Privacy-Constrained Mechanisms

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  • Eliaz, Kfir
  • Eilat, Ran
  • Mu, Xiaosheng

Abstract

Modern information technologies make it possible to store, analyze and trade unprecedented amounts of detailed information about individuals. This has led to public discussions on whether individuals' privacy should be better protected by restricting the amount or the precision of information that is collected by commercial institutions on its participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy and applying it to the design of optimal mechanisms. Specifically, we define the loss of privacy associated with a mechanism as the difference between the designer's prior and posterior beliefs about an agent's type, where this difference is calculated using Kullback-Leibler divergence, and where the change in beliefs is triggered by actions taken by the agent in the mechanism. We consider both ex-ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ) and ex-post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ) measures of privacy loss. Using these notions we study the properties of optimal privacy-constrained mechanisms and the relation between welfare/profits and privacy levels.

Suggested Citation

  • Eliaz, Kfir & Eilat, Ran & Mu, Xiaosheng, 2019. "Optimal Privacy-Constrained Mechanisms," CEPR Discussion Papers 13536, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13536
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    References listed on IDEAS

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    Cited by:

    1. Charlson, G., 2022. "Digital Gold? Pricing, Inequality and Participation in Data Markets," Janeway Institute Working Papers 2225, Faculty of Economics, University of Cambridge.
    2. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2022. "An Approximation Scheme for Data Monetization," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2412-2428, June.
    3. Annie Liang & Erik Madsen, 2020. "Data and Incentives," Papers 2006.06543, arXiv.org, revised Sep 2022.
    4. Charlson, G., 2022. "Digital gold? Pricing, inequality and participation in data markets," Cambridge Working Papers in Economics 2258, Faculty of Economics, University of Cambridge.

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    Keywords

    Privacy; Mechanism-design;

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