SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Collusion-resistant mechanisms for single-parameter agents
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Algorithmic Game Theory
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Optimal mechanism design and money burning
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Multi-parameter mechanism design and sequential posted pricing
Proceedings of the forty-second ACM symposium on Theory of computing
Approximate privacy: foundations and quantification (extended abstract)
Proceedings of the 11th ACM conference on Electronic commerce
Practical universal random sampling
IWSEC'10 Proceedings of the 5th international conference on Advances in information and computer security
Proceedings of the 12th ACM conference on Electronic commerce
When random sampling preserves privacy
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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
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
A theory of pricing private data
Proceedings of the 16th International Conference on Database Theory
Privacy and coordination: computing on databases with endogenous participation
Proceedings of the fourteenth ACM conference on Electronic commerce
ACM SIGecom Exchanges
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 consider a scenario in which a database stores sensitive data of users and an analyst wants to estimate statistics of the data. The users may suffer a cost when their data are used in which case they should be compensated. The analyst wishes to get an accurate estimate, while the users want to maximize their utility. We want to design a mechanism that can estimate statistics accurately without compromising users' privacy. Since users' costs and sensitive data may be correlated, it is important to protect the privacy of both data and cost. We model this correlation by assuming that a user's unknown sensitive data determines a distribution from a set of publicly known distributions and a user's cost is drawn from that distribution. We propose a stronger model of privacy preserving mechanism where users are compensated whenever they reveal information about their data to the mechanism. In this model, we design a Bayesian incentive compatible and privacy preserving mechanism that guarantees accuracy and protects the privacy of both cost and data.