Résumé
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 Rényi
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.