A Survey on Gradient Inversion: Attacks, Defenses and Future Directions

15 June 2022

Papers citing "A Survey on Gradient Inversion: Attacks, Defenses and Future Directions"

24 / 24 papers shown

Title
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 36 0 0 14 Apr 2025
How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks M. Prabhakaran S. Nicolazzo Antonino Nocera Vinod Puthuvath AAML MU 91 0 0 26 Mar 2025
Intermediate Outputs Are More Sensitive Than You Think Tao Huang Qingyu Huang Jiayang Meng AAML 70 1 0 01 Dec 2024
Optimal Defenses Against Gradient Reconstruction Attacks Yuxiao Chen Gamze Gürsoy Qi Lei FedML AAML 21 0 0 06 Nov 2024
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion Xuan Liu Siqi Cai Qihua Zhou Song Guo Ruibin Li Kaiwei Lin DiffM AAML 18 1 0 07 Jul 2024
Optimally Improving Cooperative Learning in a Social Setting Shahrzad Haddadan Cheng Xin Jie Gao 16 0 0 31 May 2024
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy Yichuan Shi Olivera Kotevska Viktor Reshniak Abhishek Singh Ramesh Raskar AAML 22 1 0 16 May 2024
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge Jin Qian Kaimin Wei Yongdong Wu Jilian Zhang Jipeng Chen Huan Bao 23 1 0 06 May 2024
Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks Yanbo Wang Jian Liang R. He AAML 14 5 0 05 Feb 2024
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng FedML AAML 25 19 0 27 Nov 2023
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems Guangjing Wang Ce Zhou Yuanda Wang Bocheng Chen Hanqing Guo Qiben Yan AAML SILM 53 3 0 20 Nov 2023
Transpose Attack: Stealing Datasets with Bidirectional Training Guy Amit Mosh Levy Yisroel Mirsky SILM AAML 29 0 0 13 Nov 2023
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation Dzung Pham Shreyas Kulkarni Amir Houmansadr 11 0 0 29 Oct 2023
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? Xiaoxiao Sun Nidham Gazagnadou Vivek Sharma Lingjuan Lyu Hongdong Li Liang Zheng 31 7 0 22 Sep 2023
Client-side Gradient Inversion Against Federated Learning from Poisoning Jiaheng Wei Yanjun Zhang Leo Yu Zhang Chao Chen Shirui Pan Kok-Leong Ong Jinchao Zhang Yang Xiang AAML 20 3 0 14 Sep 2023
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks Daniel Scheliga Patrick Mäder M. Seeland FedML AAML 13 5 0 08 Sep 2023
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization Hao Fang Bin Chen Xuan Wang Zhi Wang Shutao Xia 43 32 0 09 Aug 2023
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions Leijie Wu Song Guo Junxiao Wang Zicong Hong Jie M. Zhang Jingren Zhou KELM 32 4 0 02 Jun 2023
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning Joshua C. Zhao A. Elkordy Atul Sharma Yahya H. Ezzeldin A. Avestimehr S. Bagchi FedML 33 12 0 27 Mar 2023
Dataset Distillation: A Comprehensive Review Ruonan Yu Songhua Liu Xinchao Wang DD 35 121 0 17 Jan 2023
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning Mingyuan Fan Cen Chen Chengyu Wang Ximeng Liu Wenmeng Zhou Jun Huang AAML FedML 19 0 0 05 Dec 2022
How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers Guangsheng Zhang B. Liu Huan Tian Tianqing Zhu Ming Ding Wanlei Zhou PILM MIACV 12 5 0 20 Oct 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 19 12 0 12 Aug 2022
Gradient Obfuscation Gives a False Sense of Security in Federated Learning Kai Yue Richeng Jin Chau-Wai Wong D. Baron H. Dai FedML 16 46 0 08 Jun 2022