Cover Image

VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tan, Juntao, Zhang, Lan, Hu, Zhonghao, Yang, Kai, Ran, Peng, Li, Bo
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

VTarbel: Targeted Label Attack with Minimal Knowledge on Detector-enhanced Vertical Federated Learning
by: Tan, Juntao, et al.
Published: (2025)

PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
by: Li, Yang, et al.
Published: (2024)

RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
by: Xu, Yang, et al.
Published: (2024)

Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
by: Zhang, Xiaojin, et al.
Published: (2025)

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
by: Piran, Fardin Jalil, et al.
Published: (2025)

Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning
by: Yang, Ze, et al.
Published: (2025)

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
by: Xu, Runhua, et al.
Published: (2025)

Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense
by: Li, Wenjie, et al.
Published: (2024)

Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs
by: Luo, Huaiying, et al.
Published: (2025)

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

Secure and Privacy-Preserving Vertical Federated Learning
by: Jin, Shan, et al.
Published: (2026)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
by: Zhang, Xiaojin, et al.
Published: (2023)

Privacy Assessment of Federated Learning using Private Personalized Layers
by: Jourdan, Théo, et al.
Published: (2021)

Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
by: Hu, Yuke, et al.
Published: (2023)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
by: Xie, Chulin, et al.
Published: (2022)

Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
by: Wang, Zhibo, et al.
Published: (2024)

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

Trustworthy Federated Learning: Privacy, Security, and Beyond
by: Chen, Chunlu, et al.
Published: (2024)

User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
by: Yang, Haowei, et al.
Published: (2025)

Privacy Attack in Federated Learning is Not Easy: An Experimental Study
by: Zhu, Hangyu, et al.
Published: (2024)

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)

Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm
by: Wang, Taotao, et al.
Published: (2025)

VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
by: Cai, Nina, et al.
Published: (2025)

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation
by: Gu, Hanlin, et al.
Published: (2024)

Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare
by: Jeong, Hyejun, et al.
Published: (2024)

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis
by: Zhan, Zhonghao, et al.
Published: (2025)

FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
by: Ye, Pukang, et al.
Published: (2025)

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
by: Yu, Lei, et al.
Published: (2024)

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

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
by: Carney, Jane, et al.
Published: (2025)

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
by: Ozturk, Ozer, et al.
Published: (2025)

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
by: Xu, Runhua, et al.
Published: (2025)

LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning
by: Yan, Zeyu, et al.
Published: (2025)

Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems
by: Si-ahmed, Ayoub, et al.
Published: (2024)

Hijack Vertical Federated Learning Models As One Party
by: Qiu, Pengyu, et al.
Published: (2022)

Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review
by: ElZemity, Adel, et al.
Published: (2024)

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
by: Liu, Wenbo, et al.
Published: (2024)

Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
by: Taiello, Riccardo, et al.
Published: (2024)

A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data
by: Negoya, Yusaku, et al.
Published: (2025)

Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
by: Yu, Honglan, et al.
Published: (2025)