  Vertical federated learning (VFL) allows an active party with labeled feature
to leverage auxiliary features from the passive parties to improve model
performance. Concerns about the private feature and label leakage in both the
training and inference phases of VFL have drawn wide research attention. In
this paper, we propose a general privacy-preserving vertical federated deep
learning framework called FedPass, which leverages adaptive obfuscation to
protect the feature and label simultaneously. Strong privacy-preserving
capabilities about private features and labels are theoretically proved (in
Theorems 1 and 2). Extensive experimental result s with different datasets and
network architectures also justify the superiority of FedPass against existing
methods in light of its near-optimal trade-off between privacy and model
performance.
