Virtual Market Design Seminar

Privacy Preserving Signals

Philipp Strack (Yale University)

Mar 11, 2024, 16:00



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 as solutions to an optimal transport problem. We discuss the economic implications of our characterization for statistical discrimination, the revelation of sensitive information in auctions, monopoly pricing, and price discrimination