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Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack. (arXiv:2211.04157v1 [cs.LG])
Nov. 9, 2022, 2:20 a.m. | Raksha Ramakrishna, György Dán
cs.CR updates on arXiv.org arxiv.org
Property inference attacks against machine learning (ML) models aim to infer
properties of the training data that are unrelated to the primary task of the
model, and have so far been formulated as binary decision problems, i.e.,
whether or not the training data have a certain property. However, in
industrial and healthcare applications, the proportion of labels in the
training data is quite often also considered sensitive information. In this
paper we introduce a new type of property inference attack …
More from arxiv.org / cs.CR updates on arXiv.org
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