Title: The Hidden Cost of Modeling $\text{P}(X)$: Membership Inference Attacks in Generative Text Classifiers
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Membership Inference Attacks, Generative Classifiers, Discriminative Classifiers, Text Classification, Utility–Privacy trade-off

Abstract: Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despite extensive research on MIAs, systematic comparisons between generative and discriminative classifiers remain limited. This work addresses this gap by first providing theoretical motivation for why generative classifiers exhibit heightened susceptibility to MIAs, then validating these insights through comprehensive empirical evaluation.
Our study encompasses discriminative, generative, and pseudo-generative text classifiers across varying training data volumes, evaluated on five benchmark datasets. Employing a diverse array of MIA strategies, we consistently demonstrate that fully generative classifiers which explicitly model the joint likelihood $P(X,Y)$ are most vulnerable to membership leakage. Furthermore, we observe that the canonical inference approach commonly used in generative classifiers significantly amplifies this privacy risk.
These findings reveal a fundamental utility-privacy trade-off inherent in classifier design, underscoring the critical need for caution when deploying generative classifiers in privacy-sensitive applications. Our results motivate future research directions in developing privacy-preserving generative classifiers that can maintain utility while mitigating membership inference vulnerabilities.

TL;DR: Generative classifiers are far more vulnerable to membership inference attacks than discriminative ones, exposing a core utility–privacy trade-off and urging privacy-preserving generative model design.