In many statistical problems, incorporating priors can significantly improve
performance. However, the use of prior knowledge in differentially private
query release has remained underexplored, despite such priors commonly being
available in the form of public datasets, such as previous US Census releases.
With the goal of releasing statistics about a private dataset, we present
PMW^Pub, which — unlike existing baselines — leverages public data drawn from
a related distribution as prior information. We provide a theoretical analysis
and an empirical evaluation on the American Community Survey (ACS) and ADULT
datasets, which shows that our method outperforms state-of-the-art methods.
Furthermore, PMW^Pub scales well to high-dimensional data domains, where
running many existing methods would be computationally infeasible.

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Author Of this post: <a href="">Terrance Liu</a>, <a href="">Giuseppe Vietri</a>, <a href="">Thomas Steinke</a>, <a href="">Jonathan Ullman</a>, <a href="">Zhiwei Steven Wu</a>

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