Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning. (arXiv:2207.12535v1 [cs.CR])

Semi-supervised learning (SSL) leverages both labeled and unlabeled data to
train machine learning (ML) models. State-of-the-art SSL methods can achieve
comparable performance to supervised learning by leveraging much fewer labeled
data. However, most existing works focus on improving the performance of SSL.
In this work, we take a different angle by studying the training data privacy
of SSL. Specifically, we propose the first data augmentation-based membership
inference attacks against ML models trained by SSL. Given a data sample and the …

arxiv attacks inference learning semi-supervised semi-supervised learning supervised learning