Mechanism Design via Differential Privacy

We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacy [15, 14], in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie. More precisely, mechanisms with differential privacy are approximate dominant strategy under arbitrary player utility functions, are automatically resilient to coalitions, and easily allow repeatability. We study several special cases of the unlimited supply auction problem, providing new results for digital goods auctions, attribute auctions, and auctions with arbitrary structural constraints on the prices. As an important prelude to developing a privacy-preserving auction mechanism, we introd...

• Type: Conference
• Year: 2007
• Where: FOCS
• Authors: Frank McSherry, Kunal Talwar
• Keywords: Arbitrary Structural Constraints, Auction, FOCS 2007, Privacy-preserving Auction Mechanism, Theoretical Computer Science