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      Graphical-model based estimation and inference for differential privacy

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          Abstract

          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.

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          Most cited references23

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          Calibrating Noise to Sensitivity in Private Data Analysis

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            Graphical Models, Exponential Families, and Variational Inference

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              Mirror descent and nonlinear projected subgradient methods for convex optimization

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                Author and article information

                Journal
                25 January 2019
                Article
                1901.09136
                ddbb392c-f592-482d-bad0-82be099257c1

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.LG cs.CR stat.ML

                Security & Cryptology,Machine learning,Artificial intelligence
                Security & Cryptology, Machine learning, Artificial intelligence

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