2 papers
cs.LG2025
The Tail Tells All: Estimating Model-Level Membership Inference Vulnerability Without Reference Models
Euodia Dodd, NataÅ¡a KrÄo, Igor Shilov +1
Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attacks require training numerous, of…
cs.LG2025
Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods
Joseph Pollock, Igor Shilov, Euodia Dodd +1
Membership inference attacks (MIAs) are widely used to empirically assess privacy risks in machine learning models, both providing model-level vulnerability metrics and identifying…