iDLG: Improved Deep Leakage from Gradients

8 January 2020

Papers citing "iDLG: Improved Deep Leakage from Gradients"

50 / 338 papers shown

Title
SoK: Towards Security and Safety of Edge AI Tatjana Wingarz Anne Lauscher Janick Edinger Dominik Kaaser Stefan Schulte Mathias Fischer 122 0 0 07 Oct 2024
Federated Learning Nodes Can Reconstruct Peers' Image Data Ethan Wilson Kai Yue Chau-Wai Wong H. Dai FedML 121 1 0 07 Oct 2024
Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System Ravi K. Madduri Zilinghan Li Tarak Nandi Kibaek Kim Minseok Ryu Alex Rodriguez 85 2 0 29 Sep 2024
Subject Data Auditing via Source Inference Attack in Cross-Silo Federated Learning Jiaxin Li Marco Arazzi Antonino Nocera Mauro Conti 89 2 0 28 Sep 2024
Privacy Attack in Federated Learning is Not Easy: An Experimental Study Hangyu Zhu Liyuan Huang Zhenping Xie FedML 85 1 0 28 Sep 2024
In-depth Analysis of Privacy Threats in Federated Learning for Medical Data B. Das M. H. Amini Yanzhao Wu 61 0 0 27 Sep 2024
Federated Learning under Attack: Improving Gradient Inversion for Batch of Images Luiz Leite Yuri Santo Bruno L. Dalmazo André Riker FedML 44 5 0 26 Sep 2024
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem Qiongxiu Li Lixia Luo Agnese Gini Changlong Ji Zhanhao Hu Xiao-Li Li Chengfang Fang Jie Shi Xiaolin Hu FedML 94 3 0 21 Sep 2024
Data Poisoning and Leakage Analysis in Federated Learning Wenqi Wei Tiansheng Huang Zachary Yahn Anoop Singhal Margaret Loper Ling Liu FedML SILM 88 0 0 19 Sep 2024
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework Zilinghan Li Shilan He Ze Yang Minseok Ryu Kibaek Kim Ravi K. Madduri FedML 225 16 0 17 Sep 2024
Exploring User-level Gradient Inversion with a Diffusion Prior Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino Bradley Malin K. Parsons Ye Wang DiffM 91 1 0 11 Sep 2024
$S^2$ NeRF: Privacy-preserving Training Framework for NeRF Bokang Zhang Yanglin Zhang Zhikun Zhang Jinglan Yang Lingying Huang Junfeng Wu 123 2 0 03 Sep 2024
DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism Xiaoyan Yu Yifan Wei Pu Li Shuaishuai Zhou Hao Peng Li Sun Liehuang Zhu Philip S. Yu FedML 122 2 0 01 Sep 2024
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective Zifan Wang Binghui Zhang Meng Pang Yuan Hong Binghui Wang FedML 120 0 0 22 Aug 2024
Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning Joon Kim Sejin Park AAML FedML 127 2 0 15 Aug 2024
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning Chenfei Nie Qiang Li Yuxin Yang Yuede Ji Binghui Wang 116 1 0 29 Jul 2024
MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models Shanghao Shi Md Shahedul Haque Abhijeet Parida Chaoyu Zhang M. Linguraru Y. T. Hou Syed Muhammad Anwar W. Lou FedML 121 4 0 13 Jul 2024
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization Wenrui Yu Qiongxiu Li Milan Lopuhaä-Zwakenberg Mads Græsbøll Christensen Richard Heusdens FedML 104 4 0 12 Jul 2024
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks Lele Zheng Yang Cao Renhe Jiang Kenjiro Taura Yulong Shen Sheng Li Masatoshi Yoshikawa AAML 114 3 0 11 Jul 2024
QBI: Quantile-based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning Micha V. Nowak Tim P. Bott David Khachaturov Frank Puppe Adrian Krenzer Amar Hekalo FedML 89 1 0 26 Jun 2024
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios Tao Huang Ziyang Chen Jiayang Meng Qingyu Huang Xu Yang Xun Yi Ibrahim Khalil MU 85 0 0 24 Jun 2024
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning Peng Kuang Zhiwei Chang Jiahui Hu Xiaoyi Pang Jiacheng Du Yongle Chen Kui Ren FedML 91 5 0 22 Jun 2024
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives Linlin Wang Tianqing Zhu Wanlei Zhou Philip S. Yu 95 3 0 16 Jun 2024
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions Laiqiao Qin Tianqing Zhu Wanlei Zhou Philip S. Yu 119 9 0 16 Jun 2024
Is Diffusion Model Safe? Severe Data Leakage via Gradient-Guided Diffusion Model Jiayang Meng Tao Huang Hong Chen Cuiping Li DiffM 82 1 0 13 Jun 2024
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients T. Eltaras Q. Malluhi Alessandro Savino S. Di Carlo Adnan Qayyum Junaid Qadir FedML 75 1 0 06 Jun 2024
Inference Attacks: A Taxonomy, Survey, and Promising Directions Feng Wu Lei Cui Shaowen Yao Shui Yu 152 2 0 04 Jun 2024
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning Zhibo Xing Zijian Zhang Ziáng Zhang Jiamou Liu Liehuang Zhu Giovanni Russello FedML 118 3 0 03 Jun 2024
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients Weijun Li Xingliang Yuan Mark Dras PILM 106 3 0 03 Jun 2024
Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud Sifat Ut Taki Spyridon Mastorakis FedML 116 1 0 02 Jun 2024
Gradient Inversion of Federated Diffusion Models Jiyue Huang Chi Hong Lydia Y. Chen Stefanie Roos FedML 90 1 0 30 May 2024
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable Martín Bertrán Shuai Tang Michael Kearns Jamie Morgenstern Aaron Roth Zhiwei Steven Wu AAML 115 19 0 30 May 2024
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense Wenjie Li K. Fan Jingyuan Zhang Hui Li Wei Yang Bryan Lim Qiang Yang AAML FedML 111 0 0 29 May 2024
DAGER: Exact Gradient Inversion for Large Language Models Ivo Petrov Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 113 7 0 24 May 2024
Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure Zehang Deng Ruoxi Sun Minhui Xue Sheng Wen S. Çamtepe Surya Nepal Yang Xiang 103 3 0 24 May 2024
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review Md. Shahin Ali M. Ahsan Lamia Tasnim Sadia Afrin Koushik Biswas Maruf Md. Sajjad Hossain Md Mahfuz Ahmed Ronok Hashan Md. Khairul Islam Shivakumar Raman 102 13 0 22 May 2024
Information Leakage from Embedding in Large Language Models Zhipeng Wan Anda Cheng Yinggui Wang Lei Wang PILM 101 5 0 20 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 92 3 0 16 May 2024
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy Feng Wang M. C. Gursoy Senem Velipasalar 103 0 0 15 May 2024
Prospects of Privacy Advantage in Quantum Machine Learning Jamie Heredge Niraj Kumar Dylan Herman Shouvanik Chakrabarti Romina Yalovetzky Shree Hari Sureshbabu Changhao Li Marco Pistoia 120 7 0 14 May 2024
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges Xianzhi Zhang Yipeng Zhou Di Wu Shazia Riaz Quan Z. Sheng Di Wu Linchang Xiao 55 6 0 03 May 2024
Recovering Labels from Local Updates in Federated Learning Huancheng Chen H. Vikalo FedML AAML 59 5 0 02 May 2024
Advances and Open Challenges in Federated Learning with Foundation Models Chao Ren Han Yu Hongyi Peng Xiaoli Tang Anran Li ... A. Tan Bo Zhao Xiaoxiao Li Zengxiang Li Qiang Yang FedML AIFin AI4CE 194 20 0 23 Apr 2024
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes Sayan Biswas Mathieu Even Anne-Marie Kermarrec Laurent Massoulie Rafael Pires Rishi Sharma M. Vos 96 4 0 15 Apr 2024
On the Efficiency of Privacy Attacks in Federated Learning Nawrin Tabassum Ka-Ho Chow Xuyu Wang Wenbin Zhang Yanzhao Wu FedML 75 3 0 15 Apr 2024
You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks Qiushi Li Yan Zhang Ju Ren Qi Li Yaoxue Zhang AAML PICV 106 26 0 05 Apr 2024
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning Hongsheng Hu Shuo Wang Tian Dong Minhui Xue AAML 105 39 0 04 Apr 2024
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning Joshua C. Zhao Ahaan Dabholkar Atul Sharma Saurabh Bagchi FedML 81 3 0 26 Mar 2024
Enabling Privacy-preserving Model Evaluation in Federated Learning via Fully Homomorphic Encryption Cem Ata Baykara Ali Burak Ünal Mete Akgün FedML 63 0 0 21 Mar 2024
Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption Yuqi Guo Lin Li Zhongxiang Zheng Hanrui Yun Ruoyan Zhang Xiaolin Chang Zhixuan Gao FedML 62 4 0 18 Mar 2024