Inverting Gradients -- How easy is it to break privacy in federated learning?

31 March 2020

Papers citing "Inverting Gradients -- How easy is it to break privacy in federated learning?"

29 / 229 papers shown

Title
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
Towards General Deep Leakage in Federated Learning Jiahui Geng Yongli Mou Feifei Li Qing Li Oya Beyan Stefan Decker Chunming Rong FedML 25 54 0 18 Oct 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 40 16 0 20 Sep 2021
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges S. Ulukus Salman Avestimehr Michael C. Gastpar S. Jafar Ravi Tandon Chao Tian FedML 35 64 0 30 Jul 2021
Defending against Reconstruction Attack in Vertical Federated Learning Jiankai Sun Yuanshun Yao Weihao Gao Junyuan Xie Chong-Jun Wang AAML FedML 24 28 0 21 Jul 2021
Gradient-Leakage Resilient Federated Learning Wenqi Wei Ling Liu Yanzhao Wu Gong Su Arun Iyengar FedML 19 81 0 02 Jul 2021
Federated Learning with Buffered Asynchronous Aggregation John Nguyen Kshitiz Malik Hongyuan Zhan Ashkan Yousefpour Michael G. Rabbat Mani Malek Dzmitry Huba FedML 33 289 0 11 Jun 2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix Maximilian Lam Gu-Yeon Wei David Brooks Vijay Janapa Reddi Michael Mitzenmacher FedML 15 63 0 10 Jun 2021
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning Liangqiong Qu Yuyin Zhou Paul Pu Liang Yingda Xia Feifei Wang Ehsan Adeli L. Fei-Fei D. Rubin FedML AI4CE 19 176 0 10 Jun 2021
Vertical Federated Learning without Revealing Intersection Membership Jiankai Sun Xin Yang Yuanshun Yao Aonan Zhang Weihao Gao Junyuan Xie Chong-Jun Wang FedML 31 37 0 10 Jun 2021
Privacy-Preserving Federated Learning on Partitioned Attributes Shuang Zhang Liyao Xiang Xi Yu Pengzhi Chu Yingqi Chen Chen Cen L. Wang FedML 25 2 0 29 Apr 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 56 244 0 29 Apr 2021
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity Mathias Parisot Balázs Pejó Dayana Spagnuelo MIACV 27 33 0 27 Apr 2021
See through Gradients: Image Batch Recovery via GradInversion Hongxu Yin Arun Mallya Arash Vahdat J. Álvarez Jan Kautz Pavlo Molchanov FedML 25 460 0 15 Apr 2021
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses Micah Goldblum Dimitris Tsipras Chulin Xie Xinyun Chen Avi Schwarzschild D. Song A. Madry Bo-wen Li Tom Goldstein SILM 27 270 0 18 Dec 2020
Privacy-preserving medical image analysis Alexander Ziller Jonathan Passerat-Palmbach T. Ryffel Dmitrii Usynin Andrew Trask ... Jason V. Mancuso Marcus R. Makowski Daniel Rueckert R. Braren Georgios Kaissis 26 8 0 10 Dec 2020
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 49 39 0 09 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 27 163 0 08 Dec 2020
Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning Hangyu Zhu Rui Wang Yaochu Jin K. Liang Jianting Ning FedML 35 46 0 25 Nov 2020
A Federated Learning Approach to Anomaly Detection in Smart Buildings Raed Abdel Sater A. Ben Hamza 17 121 0 20 Oct 2020
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 14 132 0 15 Oct 2020
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images Ming Y. Lu Dehan Kong Jana Lipkova Richard J. Chen Rajendra Singh Drew F. K. Williamson Tiffany Y. Chen Faisal Mahmood FedML MedIm 31 168 0 21 Sep 2020
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping Liam H. Fowl Yifan Jiang W. Czaja Gavin Taylor Michael Moeller Tom Goldstein AAML 21 215 0 04 Sep 2020
Byzantine-Resilient Secure Federated Learning Jinhyun So Başak Güler A. Avestimehr FedML 19 238 0 21 Jul 2020
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks Lixin Fan Kam Woh Ng Ce Ju Tianyu Zhang Chang Liu Chee Seng Chan Qiang Yang MIACV 17 63 0 20 Jun 2020
Introducing the VoicePrivacy Initiative N. Tomashenko B. M. L. Srivastava Xin Wang Emmanuel Vincent A. Nautsch ... Nicholas W. D. Evans J. Patino J. Bonastre Paul-Gauthier Noé Massimiliano Todisco 40 128 0 04 May 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 23 146 0 22 Apr 2020
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning Jinhyun So Başak Güler A. Avestimehr FedML 27 289 0 11 Feb 2020