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 postprocessing 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.
Micheal Hay et al carefully chooses a set of queries to evaluate, and then exploits consistency constraints that should hold over the noisy output REF .
Boosting the Accuracy of Differentially Private Histograms Through Consistency
PVLDB
PVLDB
Computer Science