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Approximately optimal mechanism design via differential privacy
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Calibrating noise to sensitivity in private data analysis
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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
Is privacy compatible with truthfulness?
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Privacy auctions for recommender systems
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
A theory of pricing private data
Proceedings of the 16th International Conference on Database Theory
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We propose a simple model where individuals in a privacy-sensitive population decide whether or not to participate in a pre-announced noisy computation by an analyst, so that the database itself is endogenously determined by individuals' participation choices. The privacy an agent receives depends both on the announced noise level, as well as how many agents choose to participate in the database. Each agent has some minimum privacy requirement, and decides whether or not to participate based on how her privacy requirement compares against her expectation of the privacy she will receive if she participates in the computation. This gives rise to a game amongst the agents, where each individual's privacy if she participates, and therefore her participation choice, depends on the choices of the rest of the population. We investigate symmetric Bayes-Nash equilibria, which in this game consist of threshold strategies, where all agents whose privacy requirements are weaker than a certain threshold participate and the remaining agents do not. We characterize these equilibria, which depend both on the noise announced by the analyst and the population size; present results on existence, uniqueness, and multiplicity; and discuss a number of surprising properties they display.