SAPAG: A Self-Adaptive Privacy Attack From Gradients

SAPAG: A Self-Adaptive Privacy Attack From Gradients

14 September 2020

Caiwen Ding

Sanguthevar Rajasekaran

Papers citing "SAPAG: A Self-Adaptive Privacy Attack From Gradients"

10 / 10 papers shown

Title
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning Yiwei Zhang R. Behnia A. Yavuz Reza Ebrahimi E. Bertino FedML 28 2 0 13 Oct 2024
On the Efficiency of Privacy Attacks in Federated Learning Nawrin Tabassum Ka-Ho Chow Xuyu Wang Wenbin Zhang Yanzhao Wu FedML 37 1 0 15 Apr 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 61 4 0 13 Feb 2024
Approximate and Weighted Data Reconstruction Attack in Federated Learning Yongcun Song Ziqi Wang Enrique Zuazua AAML FedML 29 4 0 13 Aug 2023
Reconstructing Training Data from Model Gradient, Provably Zihan Wang Jason D. Lee Qi Lei FedML 32 24 0 07 Dec 2022
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction Mohamed Suliman D. Leith SILM FedML 26 7 0 30 Oct 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 42 12 0 12 Aug 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
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning M. Dahlgaard Morten Wehlast Jorgensen N. Fuglsang Hiba Nassar FedML AAML 38 2 0 25 Apr 2022
A Secure and Efficient Federated Learning Framework for NLP Jieren Deng Chenghong Wang Xianrui Meng Yijue Wang Ji Li Sheng Lin Shuo Han Fei Miao Sanguthevar Rajasekaran Caiwen Ding FedML 77 22 0 28 Jan 2022