Understanding Disparate Effects of Membership Inference Attacks and their Countermeasures

Abstract

Machine learning algorithms, when applied to sensitive data, can pose severe threats to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose whether specific private data samples are present in the training data to an attacker. However, most existing studies on MIA focus on aggregated privacy leakage for an entire population, while leaving privacy leakage across different demographic subgroups (e.g., females and males) in the population largely unexplored. This raises two important issues: (1) privacy unfairness (i.e., if some subgroups are more vulnerable to MIAs than the others); and (2) defense unfairness (i.e., if the defense mechanisms provide more protection to some particular subgroups than the others).