אמינות הערכת פגיעות חברות

תקציר מקורי באנגליתarXiv:2605.25819v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned from the data. The MI vulnerability is often evaluated through a binary classifier that tries to predict whether a particular sample was in the training data. In order to evaluate the effectiveness of MIAs multiple \textit{shadow models} are trained using random partitions of a larger dataset. After training the shadow models the MI vulnerability can be evaluated for all the samples for which we obtained shadow models. In order to evaluate the MI vulnerability reliably one needs a lot of shadow models which can be computationally infeasible. Therefore instead of re