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Poster
Graphical-model based estimation and inference for differential privacy
Ryan McKenna · Daniel Sheldon · Gerome Miklau

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #171
in Posters Tue »

Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.

Keywords: Convex Optimization · Privacy-preserving Statistics and Machine Learning

Author Information

Ryan McKenna (UMass Amherst)
Daniel Sheldon (University of Massachusetts Amherst)
Gerome Miklau (University of Massachusetts, Amherst)

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