Publications (6)
Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics
Shuyu Chen, Mingxun Zhou, Haoyu Niu +2
Secure data join enables two parties with vertically distributed data to securely compute the joined table, allowing the parties to perform downstream Secure multi-party computatio…
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…
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
Wenqiang Ruan, Xin Lin, Ruisheng Zhou +3
Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with pr…
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…
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…
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…