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Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks
[article]
2018
arXiv
pre-print
Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We perform a comprehensive analysis of white-box privacy inference attacks on deep learning models. We measure the privacy leakage by leveraging the final model parameters as well as the parameter updates during the training and fine-tuning processes. We design the attacks in the stand-alone and federated settings, with respect to passive and active inference attackers, and
arXiv:1812.00910v1
fatcat:tldxiwgvhzaalesqcjgps3zx54