Mechanism Design in Large Games: Incentives and Privacy
研究在大量参与者且个体影响微小的博弈中，如何设计同时满足激励相容和隐私保护的机制，发现隐私保护可在不增加激励成本的情况下实现。
We study the design of mechanisms satisfying a novel desideratum: privacy. This requires the mechanism not reveal 'much' about any agent's type to other agents. We propose the notion of joint differential privacy: a variant of differential privacy used in the privacy literature. We show by construction that mechanisms satisfying our desiderata exist when there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our results imply that in large economies, privacy concerns of agents can be accommodated at no additional 'cost' to standard incentive concerns.