Bayesian privacy

Ran Eilat (), Kfir Eliaz () and Xiaosheng Mu ()
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Ran Eilat: Department of Economics, Ben-Gurion University of the Negev
Kfir Eliaz: Department of Economics, Tel-Aviv University and University of Utah
Xiaosheng Mu: Department of Economics, Princeton University

Theoretical Economics, 2021, vol. 16, issue 4

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 their participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy in a mechanism. 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-post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ) and ex-ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ) measures of privacy loss. Applying these notions to the monopolistic screening environment of Mussa and Rosen (1978), we study the properties of optimal privacy-constrained mechanisms and the relation between welfare/profits and privacy levels.

Keywords: Privacy; mechanism design; relative entropy (search for similar items in EconPapers)
JEL-codes: C72 D82 D83 (search for similar items in EconPapers)
Date: 2021-11-08
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Citations: View citations in EconPapers (1)

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