Exact Inference with Approximate Computation for Differentially Private Data via Perturbations
Exact Inference with Approximate Computation for Differentially Private Data via Perturbations
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DOI：
10.29012/jpc.797
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Ruobin Gong
中科院分区：
文献类型：
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作者：
Ruobin Gong
本文讨论了如何采用两类近似计算算法，以模块化的方式，从差分私有数据产品中获得精确的统计推断。考虑了贝叶斯推理的近似贝叶斯计算和似然推理的蒙特卡洛期望最大化。在蒙特卡罗误差范围内，从这些算法得出的推断对于分析人员的原始数据模型和管理员的差异隐私机制的联合规范来说是精确的。重点是精确数据的近似计算和近似数据的精确计算之间的对偶性，这可以通过设计良好的统计推断计算过程来利用。
This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products. Considered are approximate Bayesian computation for Bayesian inference, and Monte Carlo Expectation-Maximization for likelihood inference. Up to Monte Carlo error, inference from these algorithms is exact with respect to the joint specification of both the analyst's original data model, and the curator's differential privacy mechanism. Highlighted is a duality between approximate computation on exact data, and exact computation on approximate data, which can be leveraged by a well-designed computational procedure for statistical inference.