Training Data Reconstruction Attacks

Latest papers

20 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers
Nov 6, 2026Ritik K. Badiya, Siladittya Manna, Ashmit Sinha +2Gradient Inversion Attacks Training Data Reconstruction Attacks
Indian Institute of Science, Bengaluru, India
TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs
Oct 22, 2026Mengying Zhang, Derui Wang, Ibrahim Khalil +3Privacy Leakage in Language Models Training Data Reconstruction Attacks
RMIT University · CSIRO · The Hong Kong University of Science and Technology +1
Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging
Oct 20, 2026Md Morshedul Islam, Hossain Shahriar, Alfredo CuzzocreaFederated Learning Training Data Reconstruction Attacks
Which Optimizer Leaks Most? Optimizer Implicit Bias as a Training-Data Privacy Controller
Oct 13, 2026Sagi Habani, Yehonathan Refael, Ofir LindenbaumTraining Data Reconstruction Attacks Model Inversion Attacks
Ben-Gurion University · Tel Aviv University · Bar Ilan University
From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds
Oct 9, 2026Henry Bourne, Robert F. AllisonTraining Data Reconstruction Attacks Differentially Private Stochastic Gradient Descent
Department of Mathematics The University of Bristol Beacon House, Queens Road, Bristol, BS8 1QU, United Kingdom
Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff
Oct 6, 2026Shailen Smith, Rasmus Torp, Adam BreuerAdversarial Robustness Model Inversion Attacks
Department of Computer Science, Dartmouth College · Department of Government, Dartmouth College
A Sharp Transition in Data Reconstruction under Differential Privacy
Sep 29, 2026Max Cairney-Leeming, Simone Bombari, Marco MondelliAdversarial Attacks Training Data Reconstruction Attacks
Institute of Science and Technology Austria
Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning
Sep 9, 2026Saeed Shariati, Mohsen Alambardar MeybodiGradient Inversion Attacks Federated Learning
Department of Applied Mathematics and Computer Science, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran
Client-Side Probing of Deleted Ridge Statistics in Federated Unlearning
Sep 3, 2026Yijun Quan, Giovanni MontanaTraining Data Reconstruction Attacks Federated Unlearning
University of Warwick
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Aug 19, 2026Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2Orthogonal Procrustes Alignment Training Data Reconstruction Attacks
Graduate School of Science and Technology, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8573, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8573, Ibaraki, Japan · Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research (TIAR), University of Tsukuba, 1-1-1, Tennodai, Tsukuba, 305-8577, Ibaraki, Japan
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
Jul 14, 2026Rasmus Torp, Shailen K. Smith, Adam BreuerModel Inversion Attacks Neural Network Robustness
Department of Computer Science Dartmouth College · Department of Computer Science & Department of Government Dartmouth College
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization
Jun 16, 2026William Kalikman, Ivo Petrov, Dimitar I. Dimitrov +1Gradient Inversion Attacks Transformer
ETH Zürich · INSAIT, Sofia University "St. Kliment Ohridski"
The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
Jun 15, 2026Chen Chen, Xiang Gao, Xianshun Wang +6Privacy-Preserving Language Models Privacy Leakage in Language Models
College of Computing and Data Science, Nanyang Technological University, Singapore · School of Cyber Science and Engineering, Wuhan University, China
From Latent Space to Training Data: Explainable Specialization in Minimal MLPs
May 25, 2026Enrique Alba, Ezequiel Lopez-RubioMultilayer Perceptrons Neural Network Interpretability
aITIS Software, University of Malaga, Spain
Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees
May 7, 2026Edward Tansley, Roy Makhlouf, Estelle Massart +1Training Data Reconstruction Attacks Random Feature Methods
Mathematical Institute, Woodstock Road, University of Oxford, Oxford, UK, OX2 6GG. · ICTEAM Institute, UCLouvain, Euler Building, Avenue Georges Lemaˆıtre, 4 - bte L4.05.01, Louvain-la-Neuve, B - 1348, Belgium.
Turning Black Box into White Box: Dataset Distillation Leaks
Mar 1, 2026Huajie Chen, Tianqing Zhu, Yuchen Zhong +7Training Data Reconstruction Attacks Dataset Distillation
City University of Macau · CISPA Helmholtz Center for Information Security · University of Technology Sydney +1
Learning to Detect Language Model Training Data via Active Reconstruction
Feb 22, 2026Junjie Oscar Yin, John X. Morris, Vitaly Shmatikov +2Membership Inference Attacks Privacy Leakage in Language Models
University of Washington · Cornell University · UC Berkeley +1
Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Feb 19, 2026Obaidullah Zaland, Sajib Mistry, Monowar BhuyanTraining Data Reconstruction Attacks Privacy-Preserving ML
Department of Computing Science, Umeå Unviersity, Umeå, SE-90187, Sweden · Curtin University, Bentley WA 6102, Australia
Protecting the Undeleted in Machine Unlearning
Feb 18, 2026Aloni Cohen, Refael Kohen, Kobbi Nissim +1Training Data Reconstruction Attacks Machine Unlearning
University of Chicago · Tel Aviv University · Dept. of Computer Science, Georgetown University
Mitigating Data Reconstruction Attacks in Federated Learning: Conditions, Bounds, and Theoretical Guarantees
Jul 23, 2024Xiaojin Zhang, Mingcong Xu, Wei Chen +1Federated Learning Training Data Reconstruction Attacks
Huazhong University of Science and Technology

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Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers

Ritik K. Badiya, Siladittya Manna, Ashmit Sinha, M Yashwanth+1

Indian Institute of Science, Bengaluru, India

Gradient Inversion Attacks Training Data Reconstruction Attacks Federated Learning Low-Rank Adaptation Federated Fine-Tuning Vision Transformer

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Parameter-efficient fine-tuning (PEFT) has been increasingly adopted to fine-tune pretrained models with low computational cost and is therefore also preferred in the federated learning paradigm to reduce computational and communication overhead. In this work, we investigate the sparsely explored problem of the risk of private data leakage in LoRA-adapted Vision Transformers (ViTs), where low-rank updates weaken the gradient signal available to a server with access to individual client updates. We show that quality of reconstruction is limited by two coupled ambiguities: reduced information in LoRA gradients and missing spatial cues from frozen positional embeddings. To address this, we propose a spatially anchored generative inversion framework that incorporates server-prescribed trainable positional embeddings into ViT blocks during fine-tuning at clients. The resulting positional gradients act as layer-wise spatial anchors, providing additional positional cues, while the generative prior keeps reconstructions on a realistic image manifold, reducing duplicate-data and patch-permutation artifacts, {which produce similar gradients as those produced when the target client images are used}. Adding our proposed spatial anchoring to GIAS improves PSNR by 10.14 dB, SSIM by 0.45, and LPIPS by 0.098 on CIFAR-10, as well as PSNR gains of 7.83 and 11.74 dB on high-resolution CelebA and CARS datasets, respectively, in the FedSGD setting. Such gains in reconstructed image quality metrics are also maintained at higher batch sizes and in FedAvg settings. These results indicate that LoRA-based PEFT does not, by itself, prevent data leakage in FL, and carefully incorporated positional embeddings can help in exposing private information.