Graphical-model based estimation and inference for differential privacy
Graphical-model based estimation and inference for differential privacy
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发表时间：
2019-01
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通讯作者：
Ryan McKenna;D. Sheldon;G. Miklau
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作者：
Ryan McKenna;D. Sheldon;G. Miklau
许多隐私机制通过噪声测量来揭示关于数据分布的高级信息。通常使用此信息来估计新查询的答案。在这项工作中，我们提供了一种方法来解决这个估计问题，有效地使用图形模型，这是特别有效的分布时，是高维的，但测量是在低维边缘。我们表明，我们的方法是更有效的比现有的估计技术的隐私文献，它可以提高许多国家的最先进的机制的准确性和可扩展性。
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.