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Poster
Graphical-model based estimation and inference for differential privacy
Ryan McKenna · Daniel Sheldon · Gerome Miklau
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.
Author Information
Ryan McKenna (UMass Amherst)
Daniel Sheldon (University of Massachusetts Amherst)
Gerome Miklau (University of Massachusetts, Amherst)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Graphical-model based estimation and inference for differential privacy »
Tue. Jun 11th 11:20 -- 11:25 PM Room Room 102
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