On the Complexity of Differentially Private Data Release: Efficient Algorithms and Hardness Results

Citation:

Cynthia Dwork, Moni Naor, Omer Reingold, Guy Rothblum, and Salil Vadhan. 2009. “On the Complexity of Differentially Private Data Release: Efficient Algorithms and Hardness Results.” In Proceedings of the 41st Annual ACM Symposium on Theory of Computing (STOC `09), 381–390. Bethesda, MD. Date Presented: 31 May–2 June. ACM Digital Library
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Abstract:

We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a "sanitization" of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. The sanitization may be in the form of an arbitrary data structure, accompanied by a computational procedure for determining approximate answers to queries on the original data set, or it may be a "synthetic data set" consisting of data items drawn from the same universe as items in the original data set; queries are carried out as if the synthetic data set were the actual input. In either case the process is non-interactive; once the sanitization has been released the original data and the curator play no further role. For the task of sanitizing with a synthetic dataset output, we map the boundary between computational feasibility and infeasibility with respect to a variety of utility measures. For the (potentially easier) task of sanitizing with unrestricted output format, we show a tight qualitative and quantitative connection between hardness of sanitizing and the existence of traitor tracing schemes.

Acknowledgements: This paper was supported, in part, by NSF grant CNS-0831289, Microsoft Research, and Harvard's Center for Research on Computation and Society.
Last updated on 01/05/2017