June 11, 2024, 4:13 a.m. | Cl\'ement Pierquin, Aur\'elien Bellet, Marc Tommasi, Matthieu Boussard

cs.CR updates on arXiv.org arxiv.org

arXiv:2312.13985v2 Announce Type: replace
Abstract: 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 …

adversary amplification arxiv compromise cs.cr cs.lg data differential privacy framework general knowledge mechanisms noise privacy secrets stat.ml utility

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