Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart
Privacy Attacks

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

20 June 2020

Papers citing "Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks"

10 / 10 papers shown

Title
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 43 23 0 20 Jul 2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility Xiaojin Zhang Wenjie Li Kai Chen Shutao Xia Qian Yang FedML 25 9 0 24 May 2023
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation Hanlin Gu Jiahuan Luo Yan Kang Lixin Fan Qiang Yang FedML 36 13 0 30 Jan 2023
Reconstructing Training Data from Model Gradient, Provably Zihan Wang Jason D. Lee Qi Lei FedML 32 24 0 07 Dec 2022
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching Cangxiong Chen Neill D. F. Campbell FedML 34 1 0 20 Oct 2022
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions Rui Zhang Song Guo Junxiao Wang Xin Xie Dacheng Tao 35 36 0 15 Jun 2022
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey Shangwei Guo Xu Zhang Feiyu Yang Tianwei Zhang Yan Gan Tao Xiang Yang Liu FedML 31 9 0 19 Dec 2021
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification Cangxiong Chen Neill D. F. Campbell FedML 17 24 0 19 Nov 2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 38 145 0 25 Oct 2021
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 6 132 0 15 Oct 2020