Leveraging Label Information for Stealthy Data Stealing in Vertical Federated Learning
We develop DMAVFL, a novel attack strategy that evades current detection mechanisms. The key idea is to integrate a discriminator with auxiliary classifier that takes a full advantage of the label information (which was completely ignored in previous attacks): on one hand, label information helps to better characterize embeddings of samples from distinct classes, yielding an improved reconstruction performance; on the other hand, computing malicious gradients with label information better mimics the honest training, making the malicious gradients indistinguishable from the honest ones, and the attack much more stealthy. Our comprehensive experiments demonstrate that DMAVFL significantly outperforms existing attacks, and successfully circumvents SOTA defenses for malicious attacks. Additional ablation studies and evaluations on other defenses further underscore the robustness and effectiveness of DMAVFL.
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