Defending Label Inference Attacks in Split Learning under Regression
  Setting
Haoze Qiu Fei Zheng Chaochao Chen Xiaolin Zheng
As a privacy-preserving method for implementing Vertical Federated Learning,
Split Learning has been extensively researched. However, numerous studies have
indicated that the privacy-preserving capability of Split Learning is
insufficient. In this paper, we primarily focus on label inference attacks in
Split Learning under regression setting, which are mainly implemented through
the gradient inversion method. To defend against label inference attacks, we
propose Random Label Extension (RLE), where labels are extended to obfuscate
the label information contained in the gradients, thereby preventing the
attacker from utilizing gradients to train an attack model that can infer the
original labels. To further minimize the impact on the original task, we
propose Model-based adaptive Label Extension (MLE), where original labels are
preserved in the extended labels and dominate the training process. The
experimental results show that compared to the basic defense methods, our
proposed defense methods can significantly reduce the attack model's
performance while preserving the original task's performance.