论文标题
Kantorovich河豚隐私机制
Kantorovich Mechanism for Pufferfish Privacy
论文作者
论文摘要
Pufferfish隐私在公开数据中实现了一组秘密对的$ε$ - 独立可区分性。本文研究了如何通过指数机制实现$ε$ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ - $ -Pufferfish，这是一种概括性噪声方案，概括了拉普拉斯噪声。结果表明，如果对Kantorovich最佳运输计划的敏感性进行校准，则披露的数据为$ε$ - Pufferfish Private。可以直接从基于秘密的数据统计数据（系统的先验知识）中获得这样的计划。足够的条件进一步放松以降低噪声能力。还证明，基于Kantorovich方法的高斯机制达到了$δ$ -Pufferfish隐私的$δ$ -Approximation。
Pufferfish privacy achieves $ε$-indistinguishability over a set of secret pairs in the disclosed data. This paper studies how to attain $ε$-pufferfish privacy by exponential mechanism, an additive noise scheme that generalizes the Laplace noise. It is shown that the disclosed data is $ε$-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 $δ$-approximation of $ε$-pufferfish privacy.
