pMPL: A Robust Multi-Party Learning Framework with a Privileged Party. - Lushan Song; Jiaxuan Wang; Zhexuan Wang; Xinyu Tu; Guopeng Lin; Wenqiang Ruan; Haoqi Wu; Weili Han (CCS, 2022)

Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization. - Guopeng Lin; Weili Han; Wenqiang Ruan; Ruisheng Zhou; Lushan Song; Bingshuai Li; Yunfeng Shao 001 (CCS, 2024)

Ruyi: A Configurable and Efficient Secure Multi-Party Learning Framework With Privileged Parties. - Lushan Song; Zhexuan Wang; Guopeng Lin; Weili Han (IEEE Trans. Inf. Forensics Secur., 2024)

Is MPC Secure? Leveraging Neural Network Classifiers to Detect Data Leakage Vulnerabilities in MPC Implementations. - Guopeng Lin; Xiaoning Du 001; Lushan Song; Weili Han; Jin Tan; Junming Ma; Wenjing Fang; Lei Wang (SP, 2025)

HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning. - Wenqiang Ruan; Xin Lin; Ruisheng Zhou; Guopeng Lin; Shui Yu; Weili Han (CoRR, 2025)

IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning. - Shuyu Chen; Guopeng Lin; Haoyu Niu; Lushan Song; Chengxun Hong; Weili Han (CoRR, 2025)