Pufferfish privacy achieves $\epsilon$-indistinguishability over a set of secret pairs in the disclosed data. This paper studies how to attain $\epsilon$-pufferfish privacy by exponential mechanism, an additive noise scheme that generalizes the Laplace noise. It is shown that the disclosed data is $\epsilon$-pufferfish private if the noise is calibrated to the sensitivity of the Kantorovich optimal transport plan. Such a plan can be obtained directly from the data statistics conditioned on the secret, the prior knowledge of the system. The sufficient condition is further relaxed to reduce the noise power. It is also proved that the Gaussian mechanism based on the Kantorovich approach attains the $\delta$-approximation of $\epsilon$-pufferfish privacy.
翻译:普费鱼类隐私在披露的数据中,对一组秘密的对子实现了$\ epsilon$-indististificable disability。本文研究如何通过指数机制实现$\ epsilon$-pufferfish 隐私,这是一个将 Laplace 噪声普遍化的添加噪音计划。显示如果根据Kantorovich最佳运输计划的敏感性校准了披露的数据,则披露的数据是$\ epsilon$-pufferfish 私人的。这种计划可以直接从以秘密为条件的数据统计中获取,这是系统先前的知识。为了降低噪音力,条件已经进一步放宽。还证明基于Kantorovich 方法的高斯机制实现了$delta$-approximation $salon-agelovich 私隐私隐性。