Privacy and Mechanism Design
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2013.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2026Performative Privacy: When Differential Privacy Maximizes Utility28 Aug 2026
- 2023Neural Auctions Compromise Bidder Information28 Feb 2023
- 2022Differentially Private Condorcet Voting27 Jun 2022
- 2022Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms10 Jan 2022