24 citations · 39 across the 4 of their papers we have counts for
4 papers
IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning
Shuyu Chen, Guopeng Lin, Haoyu Niu +3
Vertical privacy-preserving machine learning (vPPML) enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it…
Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization
Guopeng Lin, Weili Han, Wenqiang Ruan +4
Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with…
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
Lushan Song, Jiaxuan Wang, Zhexuan Wang +5
In order to perform machine learning among multiple parties while protecting the privacy of raw data, privacy-preserving machine learning based on secure multi-party computation (M…
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
Lushan Song, Guopeng Lin, Jiaxuan Wang +3
Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The…