Reconstructing Training Data from Model Gradient, Provably

7 December 2022

Papers citing "Reconstructing Training Data from Model Gradient, Provably"

20 / 20 papers shown

Title
Online Learning and Unlearning Yaxi Hu Bernhard Schölkopf Amartya Sanyal MU OnRL 43 0 0 13 May 2025
Personalized Federated Training of Diffusion Models with Privacy Guarantees Kumar Kshitij Patel Weitong Zhang Lingxiao Wang MedIm 50 0 0 01 Apr 2025
Gradient Inversion Attack on Graph Neural Networks Divya Anand Sinha Yezi Liu Ruijie Du Yanning Shen FedML 71 0 0 29 Nov 2024
Network Inversion and Its Applications Pirzada Suhail Hao Tang Amit Sethi AAML 65 0 0 26 Nov 2024
Optimal Defenses Against Gradient Reconstruction Attacks Yuxiao Chen Gamze Gürsoy Qi Lei FedML AAML 31 0 0 06 Nov 2024
Network Inversion for Training-Like Data Reconstruction Pirzada Suhail Amit Sethi FedML 24 0 0 22 Oct 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 28 0 0 06 Jun 2024
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients Weijun Li Qiongkai Xu Mark Dras PILM 32 1 0 03 Jun 2024
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 41 1 0 01 Jun 2024
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable Martín Bertrán Shuai Tang Michael Kearns Jamie Morgenstern Aaron Roth Zhiwei Steven Wu AAML 34 5 0 30 May 2024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks Sayedeh Leila Noorbakhsh Binghui Zhang Yuan Hong Binghui Wang AAML 25 8 0 04 Mar 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 61 4 0 13 Feb 2024
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning Jianwei Li Sheng Liu Qi Lei PILM SILM AAML 30 4 0 10 Dec 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions Haobo Zhang Junyuan Hong Yuyang Deng M. Mahdavi Jiayu Zhou FedML 67 6 0 22 Sep 2023
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning Yavuz Faruk Bakman D. Yaldiz Yahya H. Ezzeldin A. Avestimehr CLL FedML 30 15 0 03 Sep 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses G. Buzaglo Niv Haim Gilad Yehudai Gal Vardi Yakir Oz Yaniv Nikankin Michal Irani 34 10 0 04 Jul 2023
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent Tongtian Zhu Fengxiang He Kaixuan Chen Mingli Song Dacheng Tao 34 15 0 05 Jun 2023
Collaborative Learning via Prediction Consensus Dongyang Fan Celestine Mendler-Dünner Martin Jaggi FedML 29 7 0 29 May 2023
Optimizing Orthogonalized Tensor Deflation via Random Tensor Theory M. Seddik Mohammed Mahfoud Merouane Debbah 27 1 0 11 Feb 2023
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability Zhao-quan Song Yitan Wang Zheng Yu Licheng Zhang FedML 23 28 0 15 Oct 2022