Document Type

Discussion Paper

Publication Date

7-3-2023

CFDP Number

2379

CFDP Pages

45

Journal of Economic Literature (JEL) Code(s)

C11, D63, D42, D83

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. These signals are equivalent to couplings, which in turn lead to a characterization of optimal privacy-preserving signals for a decisionmaker. We demonstrate the applications of this characterization in the contexts of algorithmic fairness, price discrimination, and information design.

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Economics Commons

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