2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CR2025
Guard-GBDT: Efficient Privacy-Preserving Approximated GBDT Training on Vertical Dataset
Anxiao Song, Shujie Cui, Jianli Bai +3
In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data o…
cs.CR2024★ 2 cited
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning
Zhibo Xing, Zijian Zhang, Zi'ang Zhang +3
Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated lea…
cs.CR2023
Mostree : Malicious Secure Private Decision Tree Evaluation with Sublinear Communication
Jianli Bai, Xiangfu Song, Xiaowu Zhang +4
A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to classify its data using a tree model from a model owner (MO) and only reveals an inference…