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Privacy‐Preserving Signals

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  • Philipp Strack
  • Kai Hao Yang

Abstract

A signal is privacy‐preserving with respect to a collection of privacy sets if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy‐preserving signals for arbitrary state space and arbitrary privacy sets. A signal is privacy‐preserving if and only if it is a garbling of a reordered quantile signal. Furthermore, distributions of posterior means induced by privacy‐preserving signals are exactly mean‐preserving contractions of that induced by the quantile signal. We discuss the economic implications of our characterization for statistical discrimination, the revelation of sensitive information in auctions and price discrimination.

Suggested Citation

  • Philipp Strack & Kai Hao Yang, 2024. "Privacy‐Preserving Signals," Econometrica, Econometric Society, vol. 92(6), pages 1907-1938, November.
    • Handle: RePEc:wly:emetrp:v:92:y:2024:i:6:p:1907-1938
    DOI: 10.3982/ECTA22017
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    References listed on IDEAS

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    1. Kolotilin, Anton, 2018. "Optimal information disclosure: a linear programming approach," Theoretical Economics, Econometric Society, vol. 13(2), May.
    2. Andreas Kleiner & Benny Moldovanu & Philipp Strack, 2021. "Extreme Points and Majorization: Economic Applications," Econometrica, Econometric Society, vol. 89(4), pages 1557-1593, July.
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    6. Shota Ichihashi, 2020. "Online Privacy and Information Disclosure by Consumers," American Economic Review, American Economic Association, vol. 110(2), pages 569-595, February.
    7. Dirk Bergemann & Tibor Heumann & Stephen Morris, 2023. "Bidder-Optimal Information Structures in Auctions," Cowles Foundation Discussion Papers 2375, Cowles Foundation for Research in Economics, Yale University.
    8. Talia Gillis & Bryce McLaughlin & Jann Spiess, 2021. "On the Fairness of Machine-Assisted Human Decisions," Papers 2110.15310, arXiv.org, revised Sep 2023.
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