Mechanism Design via Differential Privacy - McSherry, Frank; Talwar, Kunal (Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2007)
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 introduce and study a generalization of previous privacy work that accommodates the high sensitivity of the auction setting, where a single participant may dramatically alter the optimal ﬁxed price, and a slight change in the offered price may take the revenue from optimal to zero.