iDLG: Improved Deep Leakage from Gradients

8 January 2020

Papers citing "iDLG: Improved Deep Leakage from Gradients"

40 / 340 papers shown

Title
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang Xiangyi Chen Min-Fong Hong Zhiwei Steven Wu Jinfeng Yi FedML 107 104 0 25 Jun 2021
Federated Learning on Non-IID Data: A Survey Hangyu Zhu Jinjin Xu Shiqing Liu Yaochu Jin OOD FedML 166 907 0 12 Jun 2021
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients Fan Mo Anastasia Borovykh Mohammad Malekzadeh Soteris Demetriou Deniz Gündüz Hamed Haddadi FedML 107 3 0 28 May 2021
Separation of Powers in Federated Learning P. Cheng Kevin Eykholt Zhongshu Gu Hani Jamjoom K.R. Jayaram Enriquillo Valdez Ashish Verma FedML 68 13 0 19 May 2021
User-Level Label Leakage from Gradients in Federated Learning A. Wainakh Fabrizio G. Ventola Till Müßig Jens Keim Carlos Garcia Cordero Ephraim Zimmer Tim Grube Kristian Kersting M. Mühlhäuser FedML AAML 88 56 0 19 May 2021
PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC) Xiaoyu Fan Guosai Wang Kung Chen Xu He Weijiang Xu 79 9 0 17 May 2021
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie SILM AAML FedML 185 107 0 02 May 2021
From Distributed Machine Learning to Federated Learning: A Survey Ji Liu Jizhou Huang Yang Zhou Xuhong Li Shilei Ji Haoyi Xiong Dejing Dou FedML OOD 184 282 0 29 Apr 2021
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It Trung D. Q. Dang Om Thakkar Swaroop Indra Ramaswamy Rajiv Mathews Peter Chin Franccoise Beaufays FedML 61 10 0 15 Apr 2021
See through Gradients: Image Batch Recovery via GradInversion Hongxu Yin Arun Mallya Arash Vahdat J. Álvarez Jan Kautz Pavlo Molchanov FedML 149 508 0 15 Apr 2021
Constrained Differentially Private Federated Learning for Low-bandwidth Devices Raouf Kerkouche G. Ács C. Castelluccia P. Genevès 80 7 0 27 Feb 2021
Proactive DP: A Multple Target Optimization Framework for DP-SGD Marten van Dijk Nhuong V. Nguyen Toan N. Nguyen Lam M. Nguyen Phuong Ha Nguyen 92 0 0 17 Feb 2021
Label Leakage and Protection in Two-party Split Learning Oscar Li Jiankai Sun Xin Yang Weihao Gao Hongyi Zhang Junyuan Xie Virginia Smith Chong-Jun Wang FedML 223 157 0 17 Feb 2021
FLOP: Federated Learning on Medical Datasets using Partial Networks Qiang Yang Jianyi Zhang Weituo Hao Gregory P. Spell Lawrence Carin FedML OOD 99 90 0 10 Feb 2021
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning Wenzhuo Yang Yipeng Zhou Maio Hu Di Wu J. Zheng Hui Wang Song Guo FedML 71 14 0 31 Jan 2021
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times Yao Fu Yipeng Zhou Di Wu Shui Yu Yonggang Wen Chao Li FedML 76 11 0 11 Jan 2021
Differentially Private Federated Learning for Cancer Prediction C. Béguier Jean Ogier du Terrail I. Meah M. Andreux Eric W. Tramel FedML 86 22 0 08 Jan 2021
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning David Enthoven Zaid Al-Ars FedML 136 15 0 01 Jan 2021
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg Yuhao Zhou Qing Ye Jiancheng Lv FedML 87 149 0 12 Dec 2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective Jingwei Sun Ang Li Binghui Wang Huanrui Yang Hai Li Yiran Chen FedML 144 177 0 08 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 387 409 0 07 Dec 2020
SSGD: A safe and efficient method of gradient descent Jinhuan Duan Xianxian Li Shiqi Gao Jinyan Wang Ziliang Zhong 67 4 0 03 Dec 2020
Privacy-preserving Collaborative Learning with Automatic Transformation Search Wei Gao Shangwei Guo Tianwei Zhang Han Qiu Yonggang Wen Yang Liu 130 53 0 25 Nov 2020
Minimal Model Structure Analysis for Input Reconstruction in Federated Learning Jia Qian Hiba Nassar Lars Kai Hansen FedML 92 9 0 29 Oct 2020
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis Xudong Pan Mi Zhang Yifan Yan Jiaming Zhu Zhemin Yang AAML 119 23 0 26 Oct 2020
Layer-wise Characterization of Latent Information Leakage in Federated Learning Fan Mo Anastasia Borovykh Mohammad Malekzadeh Hamed Haddadi Soteris Demetriou FedML 95 34 0 17 Oct 2020
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 105 144 0 15 Oct 2020
TextHide: Tackling Data Privacy in Language Understanding Tasks Yangsibo Huang Zhao Song Danqi Chen Keqin Li Sanjeev Arora FedML 67 57 0 12 Oct 2020
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Enmao Diao Jie Ding Vahid Tarokh FedML 177 598 0 03 Oct 2020
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training Yan Kang Yang Liu Xinle Liang FedML 145 58 0 25 Aug 2020
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets Ang Li Jingwei Sun Binghui Wang Lin Duan Sicheng Li Yiran Chen H. Li FedML 130 137 0 07 Aug 2020
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma Yi-Cheng Wang FedML 169 12 0 14 Jul 2020
Dataset Condensation with Gradient Matching Bo Zhao Konda Reddy Mopuri Hakan Bilen DD 265 547 0 10 Jun 2020
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data Xinwei Zhang Mingyi Hong S. Dhople W. Yin Yang Liu FedML 161 242 0 22 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 308 29 0 27 Apr 2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning Wenqi Wei Ling Liu Margaret Loper Ka-Ho Chow Mehmet Emre Gursoy Stacey Truex Yanzhao Wu FedML 119 153 0 22 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 101 55 0 01 Apr 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 251 1,338 0 31 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 354 461 0 04 Mar 2020
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud Shuang Zhang Liyao Xiang Congcong Li Yixuan Wang Quanshi Zhang Zeyu Liu Yue Liu FedML 73 1 0 18 Dec 2019