On the Privacy Effect of Data Enhancement via the Lens of Memorization. (arXiv:2208.08270v1 [cs.LG])

Machine learning poses severe privacy concerns as it is shown that the
learned models can reveal sensitive information about their training data. Many
works have investigated the effect of widely-adopted data augmentation (DA) and
adversarial training (AT) techniques, termed data enhancement in the paper, on
the privacy leakage of machine learning models. Such privacy effects are often
measured by membership inference attacks (MIAs), which aim to identify whether
a particular example belongs to the training set or not. We propose …

data lg privacy