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

18 August 2022

Li Wang

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

10 / 10 papers shown

Title
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 37 0 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 24 1 0 12 Jun 2024
Reinforcement Unlearning Dayong Ye Tianqing Zhu Congcong Zhu Derui Wang Zewei Shi Sheng Shen Wanlei Zhou Jason Xue MU 21 7 0 26 Dec 2023
Privacy-Preserving Detection Method for Transmission Line Based on Edge Collaboration Quan Shi Kaiyuan Deng 6 1 0 17 Aug 2023
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning Chuan Ma Jun Li Kang Wei Bo Liu Ming Ding Long Yuan Zhu Han H. Vincent Poor 39 42 0 18 Feb 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 346 0 13 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 348 0 25 Sep 2021
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 55 182 0 22 Apr 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 91 110 0 25 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 59 57 0 09 Feb 2021