Data Reconstruction Attacks and Defenses: A Systematic Evaluation

13 February 2024

Sheng Liu

Papers citing "Data Reconstruction Attacks and Defenses: A Systematic Evaluation"

43 / 43 papers shown

Title
Optimal Defenses Against Gradient Reconstruction Attacks Yuxiao Chen Gamze Gürsoy Qi Lei FedML AAML 81 0 0 06 Nov 2024
Multi-step Jailbreaking Privacy Attacks on ChatGPT Haoran Li Dadi Guo Wei Fan Mingshi Xu Jie Huang Fanpu Meng Yangqiu Song SILM 91 341 0 11 Apr 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 52 40 0 14 Feb 2023
Reconstructing Training Data from Model Gradient, Provably Zihan Wang Jason D. Lee Qi Lei FedML 55 25 0 07 Dec 2022
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis Sanjay Kariyappa Chuan Guo Kiwan Maeng Wenjie Xiong G. E. Suh Moinuddin K. Qureshi Hsien-Hsin S. Lee FedML 78 29 0 12 Sep 2022
Reconstructing Training Data from Trained Neural Networks Niv Haim Gal Vardi Gilad Yehudai Ohad Shamir Michal Irani 76 138 0 15 Jun 2022
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage Zhuohang Li Jiaxin Zhang Lu Liu Jian-Dong Liu FedML 69 118 0 29 Mar 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 63 40 0 15 Feb 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 180 97 0 01 Feb 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 125 54 0 28 Jan 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 97 186 0 06 Dec 2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 71 274 0 30 Nov 2021
Bayesian Framework for Gradient Leakage Mislav Balunović Dimitar I. Dimitrov Robin Staab Martin Vechev FedML 50 42 0 08 Nov 2021
Gradient Inversion with Generative Image Prior Jinwoo Jeon Jaechang Kim Kangwook Lee Sewoong Oh Jungseul Ok 58 155 0 28 Oct 2021
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 112 148 0 25 Oct 2021
See through Gradients: Image Batch Recovery via GradInversion Hongxu Yin Arun Mallya Arash Vahdat J. Álvarez Jan Kautz Pavlo Molchanov FedML 78 472 0 15 Apr 2021
SAPAG: A Self-Adaptive Privacy Attack From Gradients Yijue Wang Jieren Deng Danyi Guo Chenghong Wang Xianrui Meng Hang Liu Caiwen Ding Sanguthevar Rajasekaran 31 35 0 14 Sep 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 68 148 0 22 Apr 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 100 1,228 0 31 Mar 2020
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 121 1,612 0 01 Nov 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 94 2,204 0 21 Jun 2019
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 91 858 0 18 Apr 2018
Group Normalization Yuxin Wu Kaiming He 228 3,654 0 22 Mar 2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 377 11,790 0 11 Jan 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 120 1,294 0 20 Dec 2017
Deep Image Prior Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky SupR 122 3,151 0 29 Nov 2017
Non-local Neural Networks Xinyu Wang Ross B. Girshick Abhinav Gupta Kaiming He OffRL 289 8,905 0 21 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 278 9,760 0 25 Oct 2017
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting Xu Sun Xuancheng Ren Shuming Ma Houfeng Wang 62 157 0 19 Jun 2017
Recovery Guarantees for One-hidden-layer Neural Networks Kai Zhong Zhao Song Prateek Jain Peter L. Bartlett Inderjit S. Dhillon MLT 170 336 0 10 Jun 2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans A. Karpathy Xi Chen Diederik P. Kingma 105 942 0 19 Jan 2017
Practical Secure Aggregation for Federated Learning on User-Held Data Keith Bonawitz Vladimir Ivanov Ben Kreuter Antonio Marcedone H. B. McMahan Sarvar Patel Daniel Ramage Aaron Segal Karn Seth FedML 74 501 0 14 Nov 2016
Towards the Science of Security and Privacy in Machine Learning Nicolas Papernot Patrick McDaniel Arunesh Sinha Michael P. Wellman AAML 77 474 0 11 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 303 4,643 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 203 6,121 0 01 Jul 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 337 7,984 0 23 May 2016
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans Diederik P. Kingma ODL 192 1,941 0 25 Feb 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 401 17,468 0 17 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,878 0 10 Dec 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.8K 77,133 0 18 May 2015
Tensor Factorization via Matrix Factorization Volodymyr Kuleshov Arun Tejasvi Chaganty Percy Liang 93 85 0 29 Jan 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,039 0 22 Dec 2014
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 453 7,661 0 03 Jul 2012