Quantifying identifiability to choose and audit $ε$ in differentially private deep learning

2 code implementations • 4 Mar 2021 • Daniel Bernau, Günther Eibl, Philip W. Grassal, Hannah Keller, Florian Kerschbaum

We transform $(\epsilon,\delta)$ to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset.

BIG-bench Machine Learning Inference Attack