The Privacy of the Analyst and the Power of the State

Citation:

Cynthia Dwork, Moni Naor, and Salil Vadhan. 2012. “The Privacy of the Analyst and the Power of the State.” In Proceedings of the 53rd Annual {IEEE} Symposium on Foundations of Computer Science (FOCS `12), 400–409. New Brunswick, NJ: IEEE. Date Presented: 20–23 October. IEEE Xplore
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Abstract:

We initiate the study of "privacy for the analyst" in differentially private data analysis. That is, not only will we be concerned with ensuring differential privacy for the data (i.e. individuals or customers), which are the usual concern of differential privacy, but we also consider (differential) privacy for the set of queries posed by each data analyst. The goal is to achieve privacy with respect to other analysts, or users of the system. This problem arises only in the context of stateful privacy mechanisms, in which the responses to queries depend on other queries posed (a recent wave of results in the area utilized cleverly coordinated noise and state in order to allow answering privately hugely many queries). We argue that the problem is real by proving an exponential gap between the number of queries that can be answered (with non-trivial error) by stateless and stateful differentially private mechanisms. We then give a stateful algorithm for differentially private data analysis that also ensures differential privacy for the analyst and can answer exponentially many queries.

Acknowledgements: This paper was supported, in part, by Google Inc., Microsoft Research Silicon Valley, and Stanford University.
Last updated on 01/05/2017