Hypothesis Testing Interpretations and Renyi Differential Privacy
Hypothesis Testing Interpretations and Renyi Differential Privacy
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发表时间：
2019-05
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通讯作者：
Borja Balle;G. Barthe;Marco Gaboardi;Justin Hsu;Tetsuya Sato
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作者：
Borja Balle;G. Barthe;Marco Gaboardi;Justin Hsu;Tetsuya Sato
差异隐私是数据隐私的事实上的标准，在公共和私人部门都有应用。一种解释差异隐私的方法是通过其统计假设检验解释，这对统计学家和社会科学家特别有吸引力。非正式地，人们不能通过观察私人机制的输出来有效地测试特定的个人是否贡献了她的数据-任何测试都不可能同时具有高意义和高威力。在这篇文章中，我们确定了一些条件，在这些条件下，根据统计发散给出的隐私定义满足类似的解释。这些条件有助于分析分歧的可区分性，并利用这些条件研究了基于Renyi散度的某些差别隐私松弛的假设检验解释。这种分析还改进了这些定义和差异隐私之间的转换规则。
Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appealing to statistician and social scientists is by means of its statistical hypothesis testing interpretation. Informally, one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. In this paper, we identify some conditions under which a privacy definition given in terms of a statistical divergence satisfies a similar interpretation. These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence. This analysis also results in an improved conversion rule between these definitions and differential privacy.