Privacy-Preserving Split Learning via Patch Shuffling over Transformers

Dixi Yao, Liyao Xiang, Hengyuan Xu, Hangyu Ye, Yingqi Chen

Abstract: We focus on the privacy-preserving problem in split learning in this work. In vanilla split learning, a neural network is split to different devices to be trained, risking leaking the private training data in the process. We novelly propose a patch shuffling scheme on transformers to preserve training data privacy, yet without degrading overall model performance. Formal privacy guarantees are provided and we further introduce the batch shuffling and the spectral shuffling schemes to enhance the guarantee. We show through experiments that our methods successfully defend the black-box, white-box, and adaptive attacks in split learning, with superior performance over baselines, and are efficient to deploy with negligible overhead compared to the vanilla split learning.