Jul 19, 2022 · We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols ...
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ ...
It is suggested that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already ...
This paper focuses on the privacy-preserving training on vertically partitioned data. We choose logistic regression (LR) as training model.
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We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols ...
Aug 3, 2024 · A new algorithm called PEVLR (Privacy-preserving and Efficient Vertical Logistic Regression) is proposed to efficiently solve vertical logistic ...
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary fea- tures from the passive parties to improve model.
Aug 19, 2023 · In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive ...
Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond · Computer Science. ArXiv · 2022.
SecureML [1] proposed a 2-party privacy-preserving machine learning framework which supports the training of linear regression, logistic re- gression and neural ...