Reconstructing Training Data from Trained Neural Networks

15 June 2022

Gal Vardi

Papers citing "Reconstructing Training Data from Trained Neural Networks"

50 / 95 papers shown

Title
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics Daniele Malpetti Marco Scutari Francesco Gualdi Jessica van Setten Sander van der Laan Saskia Haitjema Aaron Mark Lee Isabelle Hering Francesca Mangili FedML AI4CE 95 0 0 12 Mar 2025
Training Set Reconstruction from Differentially Private Forests: How Effective is DP? Alice Gorgé Julien Ferry Sébastien Gambs Thibaut Vidal 60 0 0 07 Feb 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 83 0 0 02 Dec 2024
Network Inversion and Its Applications Pirzada Suhail Hao Tang Amit Sethi AAML 61 0 0 26 Nov 2024
On the Reconstruction of Training Data from Group Invariant Networks Ran Elbaz Gilad Yehudai Meirav Galun Haggai Maron 56 0 0 25 Nov 2024
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models Olivia Ma Jonathan Passerat-Palmbach Dmitrii Usynin 65 0 0 24 Nov 2024
Slowing Down Forgetting in Continual Learning Pascal Janetzky Tobias Schlagenhauf Stefan Feuerriegel CLL 19 0 0 11 Nov 2024
Network Inversion for Training-Like Data Reconstruction Pirzada Suhail Amit Sethi FedML 19 0 0 22 Oct 2024
Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications Heng Xu Tianqing Zhu Wanlei Zhou MU AAML 13 1 0 14 Oct 2024
Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models Theo Putterman Derek Lim Yoav Gelberg Stefanie Jegelka Haggai Maron AI4CE 43 5 0 05 Oct 2024
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks Judah Goldfeder Quinten Roets Gabe Guo John Wright Hod Lipson 18 1 0 27 Sep 2024
Global Outlier Detection in a Federated Learning Setting with Isolation Forest Daniele Malpetti Laura Azzimonti FedML 18 0 0 20 Sep 2024
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator Kangyang Luo Shuai Wang Xiang Li Yunshi Lan Ming Gao Jinlong Shu FedML 15 1 0 11 Sep 2024
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm? Rui Wen Michael Backes Yang Zhang TDI AAML 23 0 0 05 Sep 2024
Differentially Private Kernel Density Estimation Erzhi Liu Jerry Yao-Chieh Hu Alex Reneau Zhao Song Han Liu 50 3 0 03 Sep 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 21 1 0 29 Aug 2024
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective Zifan Wang Binghui Zhang Meng Pang Yuan Hong Binghui Wang FedML 22 0 0 22 Aug 2024
Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion Adi Haviv Shahar Sarfaty Uri Y. Hacohen N. Elkin-Koren Roi Livni Amit H. Bermano 27 2 0 15 Aug 2024
Reconstructing Training Data From Real World Models Trained with Transfer Learning Yakir Oz Gilad Yehudai Gal Vardi Itai Antebi Michal Irani Niv Haim 22 2 0 22 Jul 2024
Novel Deep Neural Network Classifier Characterization Metrics with Applications to Dataless Evaluation Nathaniel R. Dean Dilip Sarkar 27 0 0 17 Jul 2024
Differentially Private Neural Network Training under Hidden State Assumption Ding Chen Chen Liu FedML 12 0 0 11 Jul 2024
QUEEN: Query Unlearning against Model Extraction Huajie Chen Tianqing Zhu Lefeng Zhang Bo Liu Derui Wang Wanlei Zhou Minhui Xue MIACV 32 2 0 01 Jul 2024
Dataset Size Recovery from LoRA Weights Mohammad Salama Jonathan Kahana Eliahu Horwitz Yedid Hoshen 23 5 0 27 Jun 2024
Data Quality in Edge Machine Learning: A State-of-the-Art Survey M. D. Belgoumri Mohamed Reda Bouadjenek Sunil Aryal Hakim Hacid 14 1 0 01 Jun 2024
Data Reconstruction: When You See It and When You Don't Edith Cohen Haim Kaplan Yishay Mansour Shay Moran Kobbi Nissim Uri Stemmer Eliad Tsfadia AAML 30 2 0 24 May 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 20 9 0 23 May 2024
Practical Dataset Distillation Based on Deep Support Vectors Hyunho Lee Junhoo Lee Nojun Kwak 11 1 0 01 May 2024
Center-Based Relaxed Learning Against Membership Inference Attacks Xingli Fang Jung-Eun Kim 22 2 0 26 Apr 2024
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models Juntaek Lim Youngeun Kwon Ranggi Hwang Kiwan Maeng Edward Suh Minsoo Rhu SyDa 19 0 0 12 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 23 22 0 05 Apr 2024
Deep Support Vectors Junhoo Lee Hyunho Lee Kyomin Hwang Nojun Kwak 25 0 0 26 Mar 2024
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures S. V. Dibbo Adam Breuer Juston S. Moore Michael Teti AAML 20 4 0 21 Mar 2024
Efficiently Computing Similarities to Private Datasets A. Backurs Zinan Lin S. Mahabadi Sandeep Silwal Jakub Tarnawski 42 4 0 13 Mar 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 26 0 0 12 Mar 2024
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu FedML 27 2 0 02 Mar 2024
Trained Random Forests Completely Reveal your Dataset Julien Ferry Ricardo Fukasawa Timothée Pascal Thibaut Vidal AAML 16 6 0 29 Feb 2024
Supervised machine learning for microbiomics: bridging the gap between current and best practices Natasha K. Dudek Mariam Chakhvadze Saba Kobakhidze Omar Kantidze Yuriy Gankin LM&MA 19 2 0 27 Feb 2024
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models Abhishek Singh Gauri Gupta Ritvik Kapila Yichuan Shi Alex Dang Sheshank Shankar Mohammed Ehab Ramesh Raskar FedML 25 0 0 25 Feb 2024
Bounding Reconstruction Attack Success of Adversaries Without Data Priors Alexander Ziller Anneliese Riess Kristian Schwethelm Tamara T. Mueller Daniel Rueckert Georgios Kaissis MIACV AAML 16 1 0 20 Feb 2024
DualView: Data Attribution from the Dual Perspective Galip Umit Yolcu Thomas Wiegand Wojciech Samek Sebastian Lapuschkin TDI FAtt 16 0 0 19 Feb 2024
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization Idan Attias Gintare Karolina Dziugaite Mahdi Haghifam Roi Livni Daniel M. Roy 17 6 0 14 Feb 2024
Is my Data in your AI Model? Membership Inference Test with Application to Face Images Daniel DeAlcala Aythami Morales Gonzalo Mancera Julian Fierrez Ruben Tolosana J. Ortega-Garcia CVBM 15 7 0 14 Feb 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 54 4 0 13 Feb 2024
Defining Neural Network Architecture through Polytope Structures of Dataset Sangmin Lee Abbas Mammadov Jong Chul Ye 46 0 0 04 Feb 2024
Ensembler: Combating model inversion attacks using model ensemble during collaborative inference Dancheng Liu Jinjun Xiong MIACV FedML AAML 19 0 0 19 Jan 2024
Recursive Distillation for Open-Set Distributed Robot Localization Kenta Tsukahara Kanji Tanaka 10 0 0 26 Dec 2023
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging Alexander Ziller Tamara T. Mueller Simon Stieger Leonhard F. Feiner Johannes Brandt R. Braren Daniel Rueckert Georgios Kaissis 46 1 0 05 Dec 2023
Generator Born from Classifier Runpeng Yu Xinchao Wang 14 4 0 05 Dec 2023
Continual Learning: Applications and the Road Forward Eli Verwimp Rahaf Aljundi Shai Ben-David Matthias Bethge Andrea Cossu ... J. Weijer Bing Liu Vincenzo Lomonaco Tinne Tuytelaars Gido M. van de Ven CLL 17 43 0 20 Nov 2023
Transpose Attack: Stealing Datasets with Bidirectional Training Guy Amit Mosh Levy Yisroel Mirsky SILM AAML 18 0 0 13 Nov 2023