Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Universally utility-maximizing privacy mechanisms
Proceedings of the forty-first annual ACM symposium on Theory of computing
Proceedings of the forty-first annual ACM symposium on Theory of computing
On the complexity of differentially private data release: efficient algorithms and hardness results
Proceedings of the forty-first 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
On the geometry of differential privacy
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
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
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
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Proceedings of the 12th 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
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Approximately optimal auctions for selling privacy when costs are correlated with data
Proceedings of the 13th ACM Conference on Electronic Commerce
Privacy-aware mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Conducting truthful surveys, cheaply
Proceedings of the 13th ACM Conference on Electronic Commerce
Lower bounds in differential privacy
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
A study of privacy and fairness in sensitive data analysis
A study of privacy and fairness in sensitive data analysis
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
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
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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In the area of privacy-preserving data mining, a differentially private mechanism intuitively encourages people to share their data because they are at little risk of revealing their own information. However, we argue that this interpretation is incomplete because external incentives are necessary for people to participate in databases, and so data release mechanisms should not only be differentially private but also compatible with incentives, otherwise the data collected may be false. We apply the notion of truthfulness from game theory to this problem. In certain settings, it turns out that existing differentially private mechanisms do not encourage participants to report their information truthfully. On the positive side, we exhibit a transformation that takes truthful mechanisms and transforms them into differentially private mechanisms that remain truthful. Our transformation applies to games where the type space is small and the goal is to optimize an insensitive quantity such as social welfare. Our transformation incurs only a small additive loss in optimality, and it is computationally efficient. Combined with the VCG mechanism, our transformation implies that there exists a differentially private, truthful, and approximately efficient mechanism for any social welfare game with small type space. We also study a model where an explicit numerical cost is assigned to the information leaked by a mechanism. We show that in this case, even differential privacy may not be strong enough of a notion to motivate people to participate truthfully. We show that mechanisms that release a perturbed histogram of the database may reveal too much information. We also show that, in general, any mechanism that outputs a synopsis that resembles the original database (such as the mechanism of Blum et al. (STOC '08)) may reveal too much information.