  Split learning is a distributed training framework that allows multiple
parties to jointly train a machine learning model over vertically partitioned
data (partitioned by attributes). The idea is that only intermediate
computation results, rather than private features and labels, are shared
between parties so that raw training data remains private. Nevertheless, recent
works showed that the plaintext implementation of split learning suffers from
severe privacy risks that a semi-honest adversary can easily reconstruct
labels. In this work, we propose \textsf{TPSL} (Transcript Private Split
Learning), a generic gradient perturbation based split learning framework that
provides provable differential privacy guarantee. Differential privacy is
enforced on not only the model weights, but also the communicated messages in
the distributed computation setting. Our experiments on large-scale real-world
datasets demonstrate the robustness and effectiveness of \textsf{TPSL} against
label leakage attacks. We also find that \textsf{TPSL} have a better
utility-privacy trade-off than baselines.
