Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Extracting Spatiotemporal Data from Gradients with Large Language Models
par: Zheng, Lele, et autres
Publié: (2024)
par: Zheng, Lele, et autres
Publié: (2024)
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
par: Zheng, Shuyuan, et autres
Publié: (2022)
par: Zheng, Shuyuan, et autres
Publié: (2022)
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
par: Kato, Fumiyuki, et autres
Publié: (2023)
par: Kato, Fumiyuki, et autres
Publié: (2023)
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
par: You, Zhichao, et autres
Publié: (2025)
par: You, Zhichao, et autres
Publié: (2025)
Federated Graph Analytics with Differential Privacy
par: Liu, Shang, et autres
Publié: (2024)
par: Liu, Shang, et autres
Publié: (2024)
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
par: Zheng, Shuyuan, et autres
Publié: (2025)
par: Zheng, Shuyuan, et autres
Publié: (2025)
GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models
par: Carletti, Vincenzo, et autres
Publié: (2025)
par: Carletti, Vincenzo, et autres
Publié: (2025)
Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks
par: Eltaras, Tamer Ahmed, et autres
Publié: (2025)
par: Eltaras, Tamer Ahmed, et autres
Publié: (2025)
Model Inversion Attack against Federated Unlearning
par: Zhou, Lei, et autres
Publié: (2025)
par: Zhou, Lei, et autres
Publié: (2025)
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
par: Guo, Pengxin, et autres
Publié: (2025)
par: Guo, Pengxin, et autres
Publié: (2025)
Federated Learning under Attack: Improving Gradient Inversion for Batch of Images
par: Leite, Luiz, et autres
Publié: (2024)
par: Leite, Luiz, et autres
Publié: (2024)
A New Federated Learning Framework Against Gradient Inversion Attacks
par: Guo, Pengxin, et autres
Publié: (2024)
par: Guo, Pengxin, et autres
Publié: (2024)
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
par: Valadi, Viktor, et autres
Publié: (2025)
par: Valadi, Viktor, et autres
Publié: (2025)
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
par: Fan, Mingyuan, et autres
Publié: (2022)
par: Fan, Mingyuan, et autres
Publié: (2022)
Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
par: Shan, Junjie, et autres
Publié: (2024)
par: Shan, Junjie, et autres
Publié: (2024)
Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents
par: Yang, Yulong, et autres
Publié: (2024)
par: Yang, Yulong, et autres
Publié: (2024)
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
par: Gong, Zirui, et autres
Publié: (2026)
par: Gong, Zirui, et autres
Publié: (2026)
Non-Linear Trajectory Modeling for Multi-Step Gradient Inversion Attacks in Federated Learning
par: Xia, Li, et autres
Publié: (2025)
par: Xia, Li, et autres
Publié: (2025)
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
par: Xu, Runhua, et autres
Publié: (2025)
par: Xu, Runhua, et autres
Publié: (2025)
Differentially Private Subspace Fine-Tuning for Large Language Models
par: Zheng, Lele, et autres
Publié: (2026)
par: Zheng, Lele, et autres
Publié: (2026)
Label Inference Attacks against Federated Unlearning
par: Wang, Wei, et autres
Publié: (2025)
par: Wang, Wei, et autres
Publié: (2025)
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
par: Diana, Francesco, et autres
Publié: (2026)
par: Diana, Francesco, et autres
Publié: (2026)
FedGIG: Graph Inversion from Gradient in Federated Learning
par: Xiao, Tianzhe, et autres
Publié: (2024)
par: Xiao, Tianzhe, et autres
Publié: (2024)
Prompt Inversion Attack against Collaborative Inference of Large Language Models
par: Qu, Wenjie, et autres
Publié: (2025)
par: Qu, Wenjie, et autres
Publié: (2025)
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
par: Yang, Yulong, et autres
Publié: (2023)
par: Yang, Yulong, et autres
Publié: (2023)
Enhancing Gradient Variance and Differential Privacy in Quantum Federated Learning
par: Phan, Duc-Thien, et autres
Publié: (2025)
par: Phan, Duc-Thien, et autres
Publié: (2025)
SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition
par: Luo, Chenxiang, et autres
Publié: (2025)
par: Luo, Chenxiang, et autres
Publié: (2025)
Protection against Source Inference Attacks in Federated Learning
par: Athanasiou, Andreas, et autres
Publié: (2026)
par: Athanasiou, Andreas, et autres
Publié: (2026)
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
par: Hu, Hongsheng, et autres
Publié: (2024)
par: Hu, Hongsheng, et autres
Publié: (2024)
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
par: Takagi, Shun, et autres
Publié: (2024)
par: Takagi, Shun, et autres
Publié: (2024)
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
par: Li, Jianwei, et autres
Publié: (2023)
par: Li, Jianwei, et autres
Publié: (2023)
On the Efficiency of Privacy Attacks in Federated Learning
par: Tabassum, Nawrin, et autres
Publié: (2024)
par: Tabassum, Nawrin, et autres
Publié: (2024)
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
par: Li, Qiongxiu, et autres
Publié: (2024)
par: Li, Qiongxiu, et autres
Publié: (2024)
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
par: Shi, Yichuan, et autres
Publié: (2024)
par: Shi, Yichuan, et autres
Publié: (2024)
PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization
par: Wang, Yidan, et autres
Publié: (2025)
par: Wang, Yidan, et autres
Publié: (2025)
Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey
par: Yang, Wencheng, et autres
Publié: (2025)
par: Yang, Wencheng, et autres
Publié: (2025)
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
par: Li, Yuecheng, et autres
Publié: (2024)
par: Li, Yuecheng, et autres
Publié: (2024)
CSVAR: Enhancing Visual Privacy in Federated Learning via Adaptive Shuffling Against Overfitting
par: Chen, Zhuo, et autres
Publié: (2025)
par: Chen, Zhuo, et autres
Publié: (2025)
Membership Reference Attack against Laplace Mechanism of Differential Privacy
par: Huang, Wen
Publié: (2024)
par: Huang, Wen
Publié: (2024)
LDPRecover: Recovering Frequencies from Poisoning Attacks against Local Differential Privacy
par: Sun, Xinyue, et autres
Publié: (2024)
par: Sun, Xinyue, et autres
Publié: (2024)
Documents similaires
-
Extracting Spatiotemporal Data from Gradients with Large Language Models
par: Zheng, Lele, et autres
Publié: (2024) -
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
par: Zheng, Shuyuan, et autres
Publié: (2022) -
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
par: Kato, Fumiyuki, et autres
Publié: (2023) -
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
par: You, Zhichao, et autres
Publié: (2025) -
Federated Graph Analytics with Differential Privacy
par: Liu, Shang, et autres
Publié: (2024)