Approximation of Pufferfish Privacy for Gaussian Priors

arXiv:2401.12391v2 Announce Type: replace-cross
Abstract: This paper studies how to approximate pufferfish privacy when the adversary's prior belief of the published data is Gaussian distributed. Using Monge's optimal transport plan, we show that $(\epsilon, \delta)$-pufferfish privacy is attained if the additive Laplace noise is calibrated to the differences in mean and variance of the Gaussian distributions conditioned on every discriminative secret pair. A typical application is the private release of the summation (or average) query, for which sufficient conditions are …

adversary arxiv cs.cr cs.it data delta distributed epsilon math.it noise privacy studies transport