Privacy and Mechanism Design

TL;DR

This survey explores how differential privacy influences mechanism design, modeling agent privacy costs, controlling mechanism stability, and enabling novel economic mechanisms beyond privacy concerns.

Contribution

It synthesizes recent advances showing differential privacy's role in modeling privacy costs and enhancing mechanism stability and design.

Findings

Differential privacy models agent privacy costs effectively.

Tools from differential privacy help control mechanism stability.

Differential privacy enables designing mechanisms in unrelated economic settings.

Abstract

This paper is a survey of recent work at the intersection of mechanism design and privacy. The connection is a natural one, but its study has been jump-started in recent years by the advent of differential privacy, which provides a rigorous, quantitative way of reasoning about the costs that an agent might experience because of the loss of his privacy. Here, we survey several facets of this study, and differential privacy plays a role in more than one way. Of course, it provides us a basis for modeling agent costs for privacy, which is essential if we are to attempt mechanism design in a setting in which agents have preferences for privacy. It also provides a toolkit for controlling those costs. However, perhaps more surprisingly, it provides a powerful toolkit for controlling the stability of mechanisms in general, which yields a set of tools for designing novel mechanisms even in economic settings completely unrelated to privacy.