Title: MIABench: Full-Pipeline Evaluation of Membership Inference Attacks
Primary area: datasets and benchmarks
Keywords: Membership Inference Attack; Machine Learning Privacy

Abstract: Membership inference attacks (MIAs) are widely used to assess a model's vulnerability to privacy leakage by determining whether specific data instances were part of its training set. Despite their significance as a privacy metric, existing evaluations of MIAs are often limited to isolated and inconsistent scenarios, hindering comprehensive comparisons and practical insights. To address this limitation, we analyze the full pipeline of training models and conducting MIAs, and present a comprehensive benchmark for evaluating various MIA methods on deep learning models. We establish a reproducible benchmark suite with code and models, leaderboards, detailed insights into the mechanisms of different MIA approaches, and practical guidance for selecting and applying MIAs effectively. This work enhances the understanding and application of MIAs, providing a solid foundation for advancing privacy-preserving machine learning research.