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On the Discredibility of Membership Inference Attacks. (arXiv:2212.02701v1 [cs.CR])
Dec. 7, 2022, 2:10 a.m. | Shahbaz Rezaei, Xin Liu
cs.CR updates on arXiv.org arxiv.org
With the wide-spread application of machine learning models, it has become
critical to study the potential data leakage of models trained on sensitive
data. Recently, various membership inference (MI) attacks are proposed that
determines if a sample was part of the training set or not. Although the first
generation of MI attacks has been proven to be ineffective in practice, a few
recent studies proposed practical MI attacks that achieve reasonable true
positive rate at low false positive rate. The …
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