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.

翻译：本文件讨论了如何以模块方式对两种大致计算算法进行调整,以便从各种不同的私人数据产品中得出准确的统计推论。考虑的大致是贝耶斯计算贝伊西亚推理法和蒙特卡洛预期-最大推理法。除了蒙特卡洛错误之外,这些算法的推论在分析员原始数据模型和保管人差异隐私权机制的共同规格方面都是准确的。重点强调的是精确数据的大致计算法与近似数据的精确计算法之间的双重性,后者可以通过精心设计的统计推理计算程序加以利用。