[论文解读] Mechanism Design in Large Games: Incentives and Privacy

Michael Kearns, Mallesh M. Pai+2 more|arXiv (Cornell University)|Jul 17, 2012

Privacy-Preserving Technologies in Data参考文献 30被引用 6

一句话总结

本文提出了一种推荐机制，用于在大型不完全信息博弈中通过联合差分隐私实现均衡，从而在不泄露敏感类型信息的情况下实现激励相容的推荐。研究表明，任何满足联合差分隐私的相关均衡算法均可作为推荐机制，确保战略激励与强群体隐私。

ABSTRACT

We study the problem of implementing equilibria of complete information games in settings of incomplete information, and address this problem using "recommender mechanisms." A recommender mechanism is one that does not have the power to enforce outcomes or to force participation, rather it only has the power to suggestion outcomes on the basis of voluntary participation. We show that despite these restrictions, recommender mechanisms can implement equilibria of complete information games in settings of incomplete information under the condition that the game is large---i.e. that there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our result follows from a novel application of differential privacy. We show that any algorithm that computes a correlated equilibrium of a complete information game while satisfying a variant of differential privacy---which we call joint differential privacy---can be used as a recommender mechanism while satisfying our desired incentive properties. Our main technical result is an algorithm for computing a correlated equilibrium of a large game while satisfying joint differential privacy. Although our recommender mechanisms are designed to satisfy game-theoretic properties, our solution ends up satisfying a strong privacy property as well. No group of players can learn "much" about the type of any player outside the group from the recommendations of the mechanism, even if these players collude in an arbitrary way. As such, our algorithm is able to implement equilibria of complete information games, without revealing information about the realized types.

研究动机与目标

• 设计适用于不完全信息大型博弈的激励相容机制，使玩家自愿遵循推荐。
• 解决在不强制结果或强制参与的情况下实现均衡的挑战。
• 确保推荐不会泄露关于个体玩家类型的显著信息，即使玩家共谋。
• 建立博弈论均衡实现与差分隐私之间的正式联系。
• 开发一种实用算法，用于在大型博弈中计算满足联合差分隐私的相关均衡。

提出的方法

• 该机制作为推荐者运行，根据玩家类型建议行动，但不强制遵守或参与。
• 它利用一种称为联合差分隐私的差分隐私变体，该变体限制了关于任何一组玩家类型的信息泄露。
• 核心方法涉及设计一种算法，以计算满足联合差分隐私的相关均衡，确保推荐既具有激励相容性又具备隐私保护性。
• 该算法确保任何玩家联盟都无法从推荐中推断出外部玩家类型的显著信息。
• 该机制的设计依赖于大型博弈结构，即单个行动对他人收益的影响可忽略不计。
• 理论分析证明，此类机制可在保护隐私的同时实现完全信息博弈的均衡。

实验结果

研究问题

• RQ1 推荐机制是否能在无强制或参与约束的情况下，在大型不完全信息博弈中实现均衡？
• RQ2 如何利用联合差分隐私确保推荐不泄露玩家类型的敏感信息？
• RQ3 在何种条件下，相关均衡计算可同时满足激励相容性和强隐私保障？
• RQ4 隐私保护机制是否仍能在大型博弈中实现战略效率？
• RQ5 差分隐私与大型博弈中均衡实现之间存在何种关系？

主要发现

• 推荐机制可在大型不完全信息设置下实现完全信息博弈的均衡，即使在自愿参与且无强制执行的情况下亦成立。
• 任何在满足联合差分隐私条件下计算相关均衡的算法，可直接用作具备所需激励特性的推荐机制。
• 该机制确保任何共谋玩家群体都无法获得关于组外任何玩家类型的显著信息。
• 该解决方案在任意共谋情况下，同时满足强博弈论激励与强隐私保护。
• 所提出的算法在联合差分隐私下计算相关均衡，实现了大型博弈中隐私保护的均衡实现。
• 该框架在差分隐私与机制设计之间建立了正式联系，表明隐私保护算法自然产生激励相容机制。