@inproceedings{wang2024statistic,
author = {Wang, Shuaiqi and Lin, Zinan and Fanti, Giulia},
title = {Statistic Maximal Leakage},
booktitle = {IEEE International Symposium on Information Theory 2024},
year = {2024},
month = {July},
abstract = {We introduce a privacy metric called statistic maximal leakage that quantifies how much a privacy mechanism leaks about a specific secret, relative to the adversary's prior information about that secret. Statistic maximal leakage is an extension of the well-known maximal leakage. Unlike maximal leakage, it protects a single, known secret. We show that statistic maximal leakage satisfies composition and post-processing properties. Additionally, we show how to efficiently compute it in the special case of deterministic data release mechanisms. We analyze two important mechanisms under statistic maximal leakage: the quantization mechanism and randomized response. We show theoretically and empirically that the quantization mechanism achieves better privacy-utility tradeoffs in the settings we study.},
url = {http://approjects.co.za/?big=en-us/research/publication/statistic-maximal-leakage/},
}