A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy

• URL: http://arxiv.org/abs/2007.05975v3
• Date: Wed, 13 Oct 2021 12:42:54 GMT
• Title: A Graph Symmetrisation Bound on Channel Information Leakage under Blowfish Privacy
• Authors: Tobias Edwards, Benjamin I. P. Rubinstein, Zuhe Zhang, Sanming Zhou
• Abstract summary: Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage.
• Score: 12.72658988801038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework's flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates tightness.

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