Assessing Per-Sample Membership Inference Vulnerability without Retraining 事件

Assessing Per-Sample Membership Inference Vulnerability without Retraining arXiv:2602.15919v2 Announce Type: replace-cross Abstract: Recent work in the privacy literature shows that sample-targeted membership inference attacks (MIAs) significantly outperform untargeted approaches by a wide margin. Motivated by this observation, we address the following question: can the privacy vulnerability of individual training points be assessed without training shadow models? We show that per-sample exposu