On Choosing the $\hat{I}¼$ Parameter in Gaussian Differential Privacy

Bogdan Kulynych, Antti Honkela

Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-D…