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arXiv · 0904.0942

Boosting the Accuracy of Differentially-Private Histograms Through Consistency

Michael Hay·Vibhor Rastogi·Gerome Miklau·Dan Suciu
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Abstract

We show that it is possible to significantly improve the accuracy of a general class of histogram queries while satisfying differential privacy. Our approach carefully chooses a set of queries to evaluate, and then exploits consistency constraints that should hold over the noisy output. In a post-processing phase, we compute the consistent input most likely to have produced the noisy output. The final output is differentially-private and consistent, but in addition, it is often much more accurate. We show, both theoretically and experimentally, that these techniques can be used for estimating the degree sequence of a graph very precisely, and for computing a histogram that can support arbitrary range queries accurately.

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Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu. 2010-07-09. Boosting the Accuracy of Differentially-Private Histograms Through Consistency. https://arxiv.org/abs/0904.0942

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