Pufferfish privacy is a flexible generalization of differential privacy that allows to model arbitrary secrets and adversary's prior knowledge about the data. Unfortunately, designing general and tractable Pufferfish mechanisms that do not compromise utility is challenging. Furthermore, this framework does not provide the composition guarantees needed for a direct use in iterative machine learning algorithms. To mitigate these issues, we introduce a R\'enyi divergence-based variant of Pufferfish and show that it allows us to extend the applicability of the Pufferfish framework. We first generalize the Wasserstein mechanism to cover a wide range of noise distributions and introduce several ways to improve its utility. We also derive stronger guarantees against out-of-distribution adversaries. Finally, as an alternative to composition, we prove privacy amplification results for contractive noisy iterations and showcase the first use of Pufferfish in private convex optimization. A common ingredient underlying our results is the use and extension of shift reduction lemmas.

Pufferfish隐私是差分隐私的一种灵活泛化，允许对任意机密信息进行建模，并考虑到对手对数据的先前知识。我们引入了一种基于Rényi散度的Pufferfish变体，并展示它的应用拓展性。我们首先推广了Wasserstein机制，涵盖了各种噪声分布并提高了其效用。我们还对抗了对手的分布外攻击，并证明了压缩有噪声迭代的隐私放大结果，并展示了在私有凸优化中Pufferfish的第一个使用。我们的结果的一个公共要素是使用和扩展转移减少引理。

Rényi Pufferfish隐私：一般性加性噪声机制及迭代的隐私增强