Center-Based Relaxed Learning Against Membership Inference Attacks

TL;DR

This paper introduces center-based relaxed learning (CRL), a new training paradigm that enhances privacy against membership inference attacks by balancing data memorization and generalization, without extra costs.

Contribution

CRL is an architecture-agnostic training method that improves privacy preservation while maintaining model performance and simplicity.

Findings

CRL effectively reduces privacy vulnerability in classification models.

CRL maintains model accuracy comparable to standard training methods.

CRL requires no additional model capacity or data for privacy enhancement.

Abstract

Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, there is still a gap between the existing defense approaches and ideal models in performance and deployment costs. In particular, we observed that the privacy vulnerability of the model is closely correlated with the gap between the model's data-memorizing ability and generalization ability. To address this, we propose a new architecture-agnostic training paradigm called center-based relaxed learning (CRL), which is adaptive to any classification model and provides privacy preservation by sacrificing a minimal or no loss of model generalizability. We emphasize that CRL can better maintain the model's consistency between member and non-member data. Through extensive experiments on standard classification datasets, we empirically show that this approach exhibits comparable performance without requiring additional model capacity or data costs.