2 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
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…