Data Reconstruction Attacks and Defenses: A Systematic Evaluation
Sheng Liu Zihan Wang Qi Lei
Reconstruction attacks and defenses are essential in understanding the data
leakage problem in machine learning. However, prior work has centered around
empirical observations of gradient inversion attacks, lacks theoretical
groundings, and was unable to disentangle the usefulness of defending methods
versus the computational limitation of attacking methods. In this work, we
propose a strong reconstruction attack in the setting of federated learning.
The attack reconstructs intermediate features and nicely integrates with and
outperforms most of the previous methods. On this stronger attack, we
thoroughly investigate both theoretically and empirically the effect of the
most common defense methods. Our findings suggest that among various defense
mechanisms, such as gradient clipping, dropout, additive noise, local
aggregation, etc., gradient pruning emerges as the most effective strategy to
defend against state-of-the-art attacks.