Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Marriage, honesty, and stability
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
The price of privacy and the limits of LP decoding
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Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
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Artificial Intelligence
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Interactive privacy via the median mechanism
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Boosting and Differential Privacy
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
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Privately releasing conjunctions and the statistical query barrier
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Approximately optimal mechanism design via differential privacy
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EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
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Conducting truthful surveys, cheaply
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The price of anarchy in games of incomplete information
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Lower bounds in differential privacy
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
Iterative constructions and private data release
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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
The Privacy of the Analyst and the Power of the State
FOCS '12 Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
Is privacy compatible with truthfulness?
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Truthful mechanisms for agents that value privacy
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Differential privacy for the analyst via private equilibrium computation
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ACM SIGecom Exchanges
Redrawing the boundaries on purchasing data from privacy-sensitive individuals
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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.