Maximal $\alpha$-Leakage for Quantum Privacy Mechanisms

arXiv:2403.14450v1 Announce Type: cross
Abstract: In this work, maximal $\alpha$-leakage is introduced to quantify how much a quantum adversary can learn about any sensitive information of data upon observing its disturbed version via a quantum privacy mechanism. We first show that an adversary's maximal expected $\alpha$-gain using optimal measurement is characterized by measured conditional R\'enyi entropy. This can be viewed as a parametric generalization of K\"onig et al.'s famous guessing probability formula [IEEE Trans. Inf. Theory, 55(9), 2009]. Then, we …

adversary alpha arxiv can cs.cr cs.it data information learn math.it measurement mechanism privacy quant-ph quantum sensitive sensitive information version work