Private, Efficient, and Accurate: Protecting Models Trained by
Multi-party Learning with Differential Privacy

Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

IEEE Symposium on Security and Privacy (IEEE S&P), 2022

18 August 2022

Li Wang

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Papers citing "Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy"

6 / 6 papers shown

Title
FuSeFL: Fully Secure and Scalable Cross-Silo Federated Learning Sahar Ghoflsaz Ghinani Elaheh Sadredini FedML 190 2 0 18 Jul 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 272 1 0 16 Feb 2025
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 331 5 0 12 Jun 2024
Reinforcement Unlearning Dayong Ye Tianqing Zhu Congcong Zhu Derui Wang Zewei Shi Sheng Shen Wanlei Zhou Jason Xue MU 524 8 0 26 Dec 2023
Privacy-Preserving Detection Method for Transmission Line Based on Edge CollaborationInternational Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2023 Quan Shi Kaiyuan Deng 100 1 0 17 Aug 2023
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed LearningProceedings of the IEEE (Proc. IEEE), 2022 Chuan Ma Jun Li Kang Wei Bo Liu Ming Ding Long Yuan Zhu Han H. Vincent Poor 317 65 0 18 Feb 2022