SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation. - Lushan Song; Haoqi Wu; Wenqiang Ruan; Weili Han (CoRR, 2020)

Digit Semantics based Optimization for Practical Password Cracking Tools. - Haodong Zhang; Chuanwang Wang; Wenqiang Ruan; Junjie Zhang; Ming Xu 006; Weili Han (ACSAC, 2021)

Privacy Compliance: Can Technology Come to the Rescue? - Wenqiang Ruan; Mingxin Xu; Haoyang Jia; Zhenhuan Wu; Lushan Song; Weili Han (IEEE Secur. Priv., 2021)

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)

Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. - Wenqiang Ruan; Mingxin Xu; Wenjing Fang; Li Wang 056; Lei Wang 152; Weili Han (SP, 2023)

Towards Understanding the fairness of differentially private margin classifiers. - Wenqiang Ruan; Mingxin Xu; Yinan Jing; Weili Han (World Wide Web (WWW), 2023)

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)

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)