Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Algorithmic Game Theory
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Beyond nash equilibrium: solution concepts for the 21st century
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Approximately-strategyproof and tractable multiunit auctions
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Approximately optimal mechanism design via differential privacy
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
Privacy-aware mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Approximately strategy-proof voting
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
The Exponential Mechanism for Social Welfare: Private, Truthful, and Nearly Optimal
FOCS '12 Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
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Recently, there has been a number of papers relating mechanism design and privacy (e.g., see [1-6]). All of these papers consider a worst-case setting where there is no probabilistic information about the players' types. In this paper, we investigate mechanism design and privacy in the Bayesian setting, where the players' types are drawn from some common distribution. We adapt the notion of differential privacy to the Bayesian mechanism design setting, obtaining Bayesian differential privacy. We also define a robust notion of approximate truthfulness for Bayesian mechanisms, which we call persistent approximate truthfulness. We give several classes of mechanisms (e.g., social welfare mechanisms and histogram mechanisms) that achieve both Bayesian differential privacy and persistent approximate truthfulness. These classes of mechanisms can achieve optimal (economic) efficiency, and do not use any payments. We also demonstrate that by considering the above mechanisms in a modified mechanism design model, the above mechanisms can achieve actual truthfulness.