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.

翻译：纵向联邦学习（VFL）允许拥有标签特征的主动方利用被动方的辅助特征来提升模型性能。VFL在训练和推理阶段面临的隐私特征与标签泄露问题已引起广泛研究关注。本文提出一种名为FedPass的通用隐私保护纵向联邦深度学习框架，通过自适应混淆技术同时保护特征与标签。理论证明（定理1与定理2）该方法具备强大的隐私保护能力。基于不同数据集和网络架构的大量实验结果表明，FedPass在隐私保护与模型性能之间实现了接近最优的权衡，相较于现有方法具有显著优越性。