Differential privacy for the analyst via private equilibrium computation

Citation:

Justin Hsu, Aaron Roth, and Jonathan Ullman. 2013. “Differential privacy for the analyst via private equilibrium computation.” In Proceedings of the 45th annual ACM symposium on Symposium on theory of computing, 341-350. Palo Alto, California, USA: ACM. DOI
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Abstract:

We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting dif- ferential privacy both for the individuals in the database and for the analysts. That is, our mechanism's answer to each query is nearly insensitive to changes in the queries asked by other analysts. Our mechanism is the first to offer differential privacy on the joint distribution over analysts' answers, providing privacy for data an- alysts even if the other data analysts collude or register multiple accounts. In some settings, we are able to achieve nearly optimal error rates (even compared to mechanisms which do not offer an- alyst privacy), and we are able to extend our techniques to handle non-linear queries. Our analysis is based on a novel view of the pri- vate query-release problem as a two-player zero-sum game, which may be of independent interest.
Acknowledgements: Supported, in part, by NSF grant CNS-1237235.
Last updated on 06/19/2013