We propose, present, and evaluate a two-party fully secure logistic regression on a vertically split dataset using an epsilon-differential privacy (DP) ...
Epsilon-Differentially Private and Fully Secure Logistic Regression on ...
ieeexplore.ieee.org › iel7
We propose, present, and evaluate a two-party fully secure logistic regression on a vertically split dataset using an epsilon-differential privacy. (DP) ...
Mar 1, 2024 · The paper proposes a two-party fully secure logistic regression using an epsilon-differential privacy (DP) mechanism. However, the specific ...
Angelo Saadeh, Vaibhavi Kumari, Stéphane Bressan: Epsilon-Differentially Private and Fully Secure Logistic Regression on Vertically Split Data.
Differential privacy (DP) has been adopted to defend inference attacks, which adds a DP noise layer on raw embeddings to protect data privacy (see, e.g., (Thapa ...
Epsilon-Differentially Private and Fully Secure Logistic Regression on Vertically Split Data. ICDIS 2022: 188-193. [+][–]. Coauthor network. maximize. Note that ...
This work addresses the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal ...
The objective of our work is to preserve differential privacy for regression models trained on vertically partitioned data. We have theoretical proofs ...
Missing: Fully | Show results with:Fully
Sep 6, 2020 · This paper aims to address two major challenges in split VFL: 1) performance degradation due to straggling clients during training; ...
Nov 24, 2023 · ... differentially private and fully secure logistic regression on a vertically split dataset. The results can also be applied to any dataset.