Fundamental Limits of Membership Inference Attacks on Machine Learning Models

Understand

Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals.

• This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models at large.
• More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks.
• We then theoretically prove that in a non-linear regression setting with overfitting learning procedures, attacks may have a high probability of success.