We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint. We establish the necessary and sufficient condition to extract features of the original data that carry as much information about a utility attribute as possible, while not revealing any information about the sensitive attribute. This problem formulation generalizes both the information bottleneck and privacy funnel problems. We adopt a local information geometry analysis that provides useful insight into information coupling and trajectory construction of spherical perturbation of probability mass functions. This analysis allows us to construct the modal decomposition of the joint distributions, divergence transfer matrices, and mutual information. By decomposing the mutual information into orthogonal modes, we obtain the locally sufficient statistics for inferences about the utility attribute, while satisfying perfect obfuscation constraint. Furthermore, we develop the notion of perfect obfuscation based on χ2-divergence and Kullback-Leibler divergence in the Euclidean information space.
On Perfect Obfuscation: Local Information Geometry Analysis / Razeghi, B.; Calmon, F. P.; Gunduz, D.; Voloshynovskiy, S.. - (2020), pp. 1-6. (Intervento presentato al convegno 2020 IEEE International Workshop on Information Forensics and Security, WIFS 2020 tenutosi a usa nel 2020) [10.1109/WIFS49906.2020.9360888].
On Perfect Obfuscation: Local Information Geometry Analysis
Gunduz D.;
2020
Abstract
We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint. We establish the necessary and sufficient condition to extract features of the original data that carry as much information about a utility attribute as possible, while not revealing any information about the sensitive attribute. This problem formulation generalizes both the information bottleneck and privacy funnel problems. We adopt a local information geometry analysis that provides useful insight into information coupling and trajectory construction of spherical perturbation of probability mass functions. This analysis allows us to construct the modal decomposition of the joint distributions, divergence transfer matrices, and mutual information. By decomposing the mutual information into orthogonal modes, we obtain the locally sufficient statistics for inferences about the utility attribute, while satisfying perfect obfuscation constraint. Furthermore, we develop the notion of perfect obfuscation based on χ2-divergence and Kullback-Leibler divergence in the Euclidean information space.
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