On Local Mutual-Information Privacy
<|reference_start|>On Local Mutual-Information Privacy: 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.<|reference_end|>
@article{ngo2024on,
    title={On Local Mutual-Information Privacy},
    author={Khac-Hoang Ngo, Johan "Ostman, and Alexandre Graell i Amat},
    journal={arXiv preprint arXiv:2405.07596},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07596},
    primaryClass={cs.IT math.IT}
}