Epsilon*: Privacy Metric for Machine Learning Models. (arXiv:2307.11280v1 [cs.LG])

We introduce Epsilon*, a new privacy metric for measuring the privacy risk of
a single model instance prior to, during, or after deployment of privacy
mitigation strategies. The metric does not require access to the training data
sampling or model training algorithm. Epsilon* is a function of true positive
and false positive rates in a hypothesis test used by an adversary in a
membership inference attack. We distinguish between quantifying the privacy
loss of a trained model instance and quantifying …

access algorithm data deployment function instance machine machine learning machine learning models measuring metric mitigation model training privacy privacy risk risk single training