Can I read FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation on EtoBox?

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation by Gu, Hanlin; Luo, Jiahuan; Kang, Yan; Fan, Lixin; Yang, Qiang is a scholarly article available to read on EtoBox.

What is FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation about?

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.

Author

Gu, Hanlin; Luo, Jiahuan; Kang, Yan; Fan, Lixin; Yang, Qiang

Published

2023

Language

EN