Dissecting Distribution Inference

arXiv:2212.07591v2 Announce Type: replace-cross
Abstract: A distribution inference attack aims to infer statistical properties of data used to train machine learning models. These attacks are sometimes surprisingly potent, but the factors that impact distribution inference risk are not well understood and demonstrated attacks often rely on strong and unrealistic assumptions such as full knowledge of training environments even in supposedly black-box threat scenarios. To improve understanding of distribution inference risks, we develop a new black-box attack that even outperforms the …

arxiv cs.ai cs.cr cs.lg distribution