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.
Recommended Citation
Strack, Philipp and Yang, Kai Hao, Privacy Preserving Signals (June 2, 2023). Available at SSRN: https://ssrn.com/abstract=4467608 or http://dx.doi.org/10.2139/ssrn.4467608