Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Single-value combinatorial auctions and algorithmic implementation in undominated strategies
Journal of the ACM (JACM)
The Differential Privacy Frontier (Extended Abstract)
TCC '09 Proceedings of the 6th Theory of Cryptography Conference on Theory of Cryptography
Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
Differential privacy in new settings
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Winner-imposing strategyproof mechanisms for multiple facility location games
WINE'10 Proceedings of the 6th international conference on Internet and network economics
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
Privacy-aware mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Buying private data at auction: the sensitive surveyor's problem
ACM SIGecom Exchanges
Is privacy compatible with truthfulness?
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Approaching utopia: strong truthfulness and externality-resistant mechanisms
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Winner-imposing strategyproof mechanisms for multiple Facility Location games
Theoretical Computer Science
Bayesian mechanism design with efficiency, privacy, and approximate truthfulness
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Take it or leave it: running a survey when privacy comes at a cost
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Truthful mechanisms for agents that value privacy
Proceedings of the fourteenth ACM conference on Electronic commerce
Privacy and coordination: computing on databases with endogenous participation
Proceedings of the fourteenth ACM conference on Electronic commerce
Strategyproof facility location for concave cost functions
Proceedings of the fourteenth ACM conference on Electronic commerce
ACM SIGecom Exchanges
On the power of deterministic mechanisms for facility location games
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
Approximate Mechanism Design without Money
ACM Transactions on Economics and Computation
Mechanism design in large games: incentives and privacy
Proceedings of the 5th conference on Innovations in theoretical computer science
Redrawing the boundaries on purchasing data from privacy-sensitive individuals
Proceedings of the 5th conference on Innovations in theoretical computer science
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We study the implementation challenge in an abstract interdependent values model and an arbitrary objective function. We design a generic mechanism that allows for approximate optimal implementation of insensitive objective functions in ex-post Nash equilibrium. If, furthermore, values are private then the same mechanism is strategy proof. We cast our results onto two specific models: pricing and facility location. The mechanism we design is optimal up to an additive factor of the order of magnitude of one over the square root of the number of agents and involves no utility transfers. Underlying our mechanism is a lottery between two auxiliary mechanisms --- with high probability we actuate a mechanism that reduces players influence on the choice of the social alternative, while choosing the optimal outcome with high probability. This is where differential privacy is employed. With the complementary probability we actuate a mechanism that may be typically far from optimal but is incentive compatible. The joint mechanism inherits the desired properties from both.