Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Understand

Deep neural networks are susceptible to various inference attacks as they remember information about their training data.

• We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models.
• We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training.
• We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge.