Generalization and Membership Inference Attack a Practical Perspective

TL;DR

This paper empirically investigates how advanced generalization techniques like augmentation and early stopping can drastically reduce the success of membership inference attacks, highlighting their practical importance.

Contribution

It provides a comprehensive empirical analysis demonstrating that improved model generalization significantly diminishes MIA success rates, with potential reductions up to 100 times.

Findings

Advanced generalization techniques can reduce MIA success by up to 100 times.

Combining augmentation and early stopping introduces randomness that further decreases attack effectiveness.

Analysis of over 1,000 models confirms the direct link between generalization and MIA performance.

Abstract

With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previously accepted assumptions. In this paper, we revisit the longstanding debate regarding the correlation between MIA success rates and model generalization using an empirical approach. We focused on employing augmentation techniques and early stopping to enhance model generalization and examined their impact on MIA success rates. We found that utilizing advanced generalization techniques can significantly decrease attack performance, potentially by up to 100 times. Moreover, combining these methods not only improves model generalization but also reduces attack effectiveness by introducing randomness during training. Additionally, our study confirmed the direct impact of generalization on MIA performance through an analysis of over 1K models in a controlled environment.