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Privacy-Preserving Multiparty Learning For Logistic Regression

Privacy-Preserving Multiparty Learning For Logistic Regression

4 October 2018
Wei Du
Ang Li
Qinghua Li
ArXiv (abs)PDFHTML

Papers citing "Privacy-Preserving Multiparty Learning For Logistic Regression"

4 / 4 papers shown
PraxiMLP: A Threshold-based Framework for Efficient Three-Party MLP with Practical Security
PraxiMLP: A Threshold-based Framework for Efficient Three-Party MLP with Practical Security
Tianle Tao
Shizhao Peng
Haogang Zhu
128
0
0
08 Nov 2025
Privacy-preserving Logistic Regression with Secret Sharing
Privacy-preserving Logistic Regression with Secret SharingBMC Medical Informatics and Decision Making (BMC Med Inform Decis Mak), 2021
Ali Reza Ghavamipour
Fatih Turkmen
Xiaoqian Jian
99
21
0
14 May 2021
Scalable Privacy-Preserving Distributed Learning
Scalable Privacy-Preserving Distributed Learning
D. Froelicher
J. Troncoso-Pastoriza
Apostolos Pyrgelis
Sinem Sav
João Sá Sousa
Jean-Philippe Bossuat
Jean-Pierre Hubaux
FedML
340
76
0
19 May 2020
Asynchronous Federated Learning with Differential Privacy for Edge
  Intelligence
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Yanan Li
Shusen Yang
Xuebin Ren
Cong Zhao
FedML
222
42
0
17 Dec 2019
1
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