Auditing Differential Privacy in High Dimensions with the Kernel Quantum R\'enyi Divergence. (arXiv:2205.13941v1 [cs.LG])

Differential privacy (DP) is the de facto standard for private data release
and private machine learning. Auditing black-box DP algorithms and mechanisms
to certify whether they satisfy a certain DP guarantee is challenging,
especially in high dimension. We propose relaxations of differential privacy
based on new divergences on probability distributions: the kernel R\'enyi
divergence and its regularized version. We show that the regularized kernel
R\'enyi divergence can be estimated from samples even in high dimensions,
giving rise to auditing procedures …

auditing differential privacy kernel lg privacy quantum