Cover Image

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yao, Duanyi, Li, Songze, Gong, Xueluan, Hou, Sizai, Pan, Gaoning
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders
by: Hou, Sizai, et al.
Published: (2024)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
by: Yao, Duanyi, et al.
Published: (2024)

TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data
by: Dai, Yanbo, et al.
Published: (2025)

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models
by: Hou, Sizai, et al.
Published: (2025)

Hidden Ads: Behavior Triggered Semantic Backdoors for Advertisement Injection in Vision Language Models
by: Yao, Duanyi, et al.
Published: (2026)

PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
by: Hou, Sizai, et al.
Published: (2024)

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, et al.
Published: (2024)

Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
by: Lee, Seohyun, et al.
Published: (2025)

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation
by: Pham, Dzung, et al.
Published: (2023)

OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing
by: Li, Songze, et al.
Published: (2024)

Planting Undetectable Backdoors in Machine Learning Models
by: Goldwasser, Shafi, et al.
Published: (2022)

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
by: Zhang, Mingxuan, et al.
Published: (2025)

Competitive Advantage Attacks to Decentralized Federated Learning
by: Jia, Yuqi, et al.
Published: (2023)

Noise as a Probe: Membership Inference Attacks on Diffusion Models Leveraging Initial Noise
by: Lian, Puwei, et al.
Published: (2026)

Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
by: Lian, Puwei, et al.
Published: (2025)

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
by: Arazzi, Marco, et al.
Published: (2024)

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)

Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
by: Liu, Yige, et al.
Published: (2026)

Hidden Data Privacy Breaches in Federated Learning
by: Gong, Xueluan, et al.
Published: (2024)

Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
by: Wu, Zhaomin, et al.
Published: (2024)

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
by: Gong, Zirui, et al.
Published: (2026)

FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
by: Long, Yuzhen, et al.
Published: (2025)

Backdoor Attack with Sparse and Invisible Trigger
by: Gao, Yinghua, et al.
Published: (2023)

Secure Vertical Federated Learning Under Unreliable Connectivity
by: Qiu, Xinchi, et al.
Published: (2023)

Label Inference Attacks against Node-level Vertical Federated GNNs
by: Arazzi, Marco, et al.
Published: (2023)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
by: Yang, Jirui, et al.
Published: (2024)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
by: Varasteh, Morteza
Published: (2023)

Model Hijacking Attack in Federated Learning
by: Li, Zheng, et al.
Published: (2024)

Decaf: Data Distribution Decompose Attack against Federated Learning
by: Dai, Zhiyang, et al.
Published: (2024)

Sybil-based Virtual Data Poisoning Attacks in Federated Learning
by: Zhu, Changxun, et al.
Published: (2025)

VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data
by: Yuan, Xun, et al.
Published: (2024)

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)

Federated Learning Nodes Can Reconstruct Peers' Image Data
by: Wilson, Ethan, et al.
Published: (2024)

Attacks on fairness in Federated Learning
by: Rance, Joseph, et al.
Published: (2023)

Approximate and Weighted Data Reconstruction Attack in Federated Learning
by: Song, Yongcun, et al.
Published: (2023)

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
by: Cheng, Tong, et al.
Published: (2025)

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
by: Diana, Francesco, et al.
Published: (2025)