InstaHide's Sample Complexity When Mixing Two Private Images

Training neural networks usually require large numbers of sensitive training data, and how to protect the privacy of training data has thus become a critical topic in deep learning research. InstaHide is a state-of-the-art scheme to protect training data privacy with only minor effects on test accuracy, and its security has become a salient question. In this paper, we systematically study recent attacks on InstaHide and present a unified framework to understand and analyze these attacks. We find that existing …

accuracy art become complexity cs.cc cs.cr cs.ds cs.lg data data privacy deep learning effects images networks neural networks numbers privacy protect research sample security state stat.ml test training training data