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arXiv · 2405.07596

On Local Mutual-Information Privacy

Khac-Hoang Ngo·Johan Östman·Alexandre Graell i Amat
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Abstract

Local mutual-information privacy (LMIP) is a privacy notion that aims to quantify the reduction of uncertainty about the input data when the output of a privacy-preserving mechanism is revealed. We study the relation of LMIP with local differential privacy (LDP), the de facto standard notion of privacy in context-independent (CI) scenarios, and with local information privacy (LIP), the state-of-the-art notion for context-dependent settings. We establish explicit conversion rules, i.e., bounds on the privacy parameters for an LMIP mechanism to also satisfy LDP/LIP, and vice versa. We use our bounds to formally verify that LMIP is a weak privacy notion. We also show that uncorrelated Gaussian noise is the best-case noise in terms of CI-LMIP if both the input data and the noise are subject to an average power constraint.

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Khac-Hoang Ngo, Johan Östman, Alexandre Graell i Amat. 2024-08-28. On Local Mutual-Information Privacy. https://arxiv.org/abs/2405.07596

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