Protecting Split Learning by Potential Energy Loss

Understand

As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.

• However, its security is constantly being questioned since the intermediate results are shared during training and inference.
• In this paper, we focus on the privacy leakage from the forward embeddings of split learning.
• Specifically, since the forward embeddings contain too much information about the label, the attacker can either use a few labeled samples to fine-tune the top model or perform unsupervised attacks such as clustering to infer the true labels from the forward embeddings.