Bayes' capacity, not the DP epsilon parameter, is shown to track how well Gaussian and von Mises-Fisher noise mechanisms resist gradient-based reconstruction attacks.
von Mises-Fisher distributions and their statistical divergence
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abstract
The von Mises-Fisher family is a parametric family of distributions on the surface of the unit ball, summarised by a concentration parameter and a mean direction. As a quasi-Bayesian prior, the von Mises-Fisher distribution is a convenient and parsimonious choice when parameter spaces are isomorphic to the hypersphere (e.g., maximum score estimation in semi-parametric discrete choice, estimation of single-index treatment assignment rules via empirical welfare maximisation, under-identifying linear simultaneous equation models). Despite a long history of application, measures of statistical divergence have not been analytically characterised for von Mises-Fisher distributions. This paper provides analytical expressions for the $f$-divergence of a von Mises-Fisher distribution from another, distinct, von Mises-Fisher distribution in $\mathbb{R}^p$ and the uniform distribution over the hypersphere. This paper also collect several other results pertaining to the von Mises-Fisher family of distributions, and characterises the limiting behaviour of the measures of divergence that we consider.
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Comparing privacy notions for protection against reconstruction attacks in machine learning
Bayes' capacity, not the DP epsilon parameter, is shown to track how well Gaussian and von Mises-Fisher noise mechanisms resist gradient-based reconstruction attacks.