Artikel
Bayesian privacy
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 K ) and ex ante (the expected difference in beliefs over all type realizations cannot exceed some threshold K ) 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.
- Language
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Englisch
- Bibliographic citation
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Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 16 ; Year: 2021 ; Issue: 4 ; Pages: 1557-1603 ; New Haven, CT: The Econometric Society
- Classification
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Wirtschaft
Market Design
Asymmetric and Private Information; Mechanism Design
- Subject
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Privacy
mechanism-design
relative entropy
- Event
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Geistige Schöpfung
- (who)
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Eilat, Ran
Eliaz, Kfir
Mu, Xiaosheng
- Event
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Veröffentlichung
- (who)
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The Econometric Society
- (where)
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New Haven, CT
- (when)
-
2021
- DOI
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doi:10.3982/TE4390
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Eilat, Ran
- Eliaz, Kfir
- Mu, Xiaosheng
- The Econometric Society
Time of origin
- 2021
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