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LDL: A Defense for Label-Based Membership Inference Attacks. (arXiv:2212.01688v1 [cs.LG])
Dec. 6, 2022, 2:10 a.m. | Arezoo Rajabi, Dinuka Sahabandu, Luyao Niu, Bhaskar Ramasubramanian, Radha Poovendran
cs.CR updates on arXiv.org arxiv.org
The data used to train deep neural network (DNN) models in applications such
as healthcare and finance typically contain sensitive information. A DNN model
may suffer from overfitting. Overfitted models have been shown to be
susceptible to query-based attacks such as membership inference attacks (MIAs).
MIAs aim to determine whether a sample belongs to the dataset used to train a
classifier (members) or not (nonmembers). Recently, a new class of label based
MIAs (LAB MIAs) was proposed, where an adversary …
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