Optimal Threshold for Explanation-based Membership Inference Attacks against Machine Learning Models

The core message of this work is to provide a sound mathematical formulation to prove the existence of an optimal explanation variance threshold that an adversary can utilize to launch membership inference attacks against machine learning models.