We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst. Such an analyst selects mechanisms (to be run on a sensitive data set) and their privacy budgets adaptively, that is, based on the answers from other mechanisms run previously on the same data set. In the language of Rogers, Roth, Ullman and Vadhan, this gives a filter for GDP with the same parameters as for nonadaptive composition.
翻译：我们发现,高斯差异隐私(Gaussian differental Previrony)是专门为分析高斯语添加的噪音而专门设计的一种差异隐私的变体,即使在完全适应性强的分析师在场的情况下,它也是优雅的。 这样的分析师选择了机制(在敏感数据集上运行 ), 并适应性地选择了隐私预算,也就是说,基于以前在同一数据集上运行的其他机制的答案。 用罗杰斯、罗思、乌尔曼和瓦德汉的语言,这为GDP提供了一个过滤器,其参数与非适应性构成的参数相同。
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