Image de couverture de livre

Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Liu, Yige, Xu, Dexuan, Guo, Zimai, Cao, Yongzhi, Wang, Hanpin
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
par: Liu, Yige, et autres
Publié: (2026)

How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
par: Wang, Jinbo, et autres
Publié: (2024)

Label Inference Attacks against Node-level Vertical Federated GNNs
par: Arazzi, Marco, et autres
Publié: (2023)

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
par: Qiu, Pengyu, et autres
Publié: (2022)

KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
par: Arazzi, Marco, et autres
Publié: (2024)

Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
par: Wang, Yujing, et autres
Publié: (2024)

FL-Defender: Combating Targeted Attacks in Federated Learning
par: Jebreel, Najeeb, et autres
Publié: (2022)

Invariant Aggregator for Defending against Federated Backdoor Attacks
par: Wang, Xiaoyang, et autres
Publié: (2022)

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

LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
par: He, Ying, et autres
Publié: (2024)

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
par: Xue, Lulu, et autres
Publié: (2024)

EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
par: Cheng, Tong, et autres
Publié: (2025)

Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
par: Lee, Seohyun, et autres
Publié: (2025)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
par: Jung, Kangsoo, et autres
Publié: (2025)

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
par: Tan, Qi, et autres
Publié: (2024)

Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack
par: Yue, Murong, et autres
Publié: (2025)

Defending against Backdoor Attack on Deep Neural Networks
par: Cheng, Hao, et autres
Publié: (2020)

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
par: Yao, Duanyi, et autres
Publié: (2024)

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
par: Li, Jiacheng, et autres
Publié: (2023)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
par: Jiang, Yu, et autres
Publié: (2025)

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
par: Lowy, Andrew, et autres
Publié: (2024)

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
par: Guo, Wenkai, et autres
Publié: (2025)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
par: Obioma, Chibueze Peace, et autres
Publié: (2025)

Auditing Privacy Mechanisms via Label Inference Attacks
par: Busa-Fekete, Róbert István, et autres
Publié: (2024)

Similarity-based Label Inference Attack against Training and Inference of Split Learning
par: Liu, Junlin, et autres
Publié: (2022)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
par: Yang, Jirui, et autres
Publié: (2024)

OSLO: One-Shot Label-Only Membership Inference Attacks
par: Peng, Yuefeng, et autres
Publié: (2024)

PackVFL: Efficient HE Packing for Vertical Federated Learning
par: Yang, Liu, et autres
Publié: (2024)

Your Agent Can Defend Itself against Backdoor Attacks
par: Changjiang, Li, et autres
Publié: (2025)

Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
par: Jebreel, Najeeb, et autres
Publié: (2026)

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
par: Luo, Xinjian, et autres
Publié: (2020)

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
par: Foroughi, Mohammad Hadi, et autres
Publié: (2026)

On the Out-of-Distribution Backdoor Attack for Federated Learning
par: Xu, Jiahao, et autres
Publié: (2025)

Federated Learning with Only Positive Labels by Exploring Label Correlations
par: An, Xuming, et autres
Publié: (2024)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
par: Yang, Jirui, et autres
Publié: (2024)

Defending Against Poisoning Attacks in Federated Learning with Blockchain
par: Dong, Nanqing, et autres
Publié: (2023)

Rethinking Membership Inference Attacks Against Transfer Learning
par: Wu, Cong, et autres
Publié: (2025)

SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
par: Amit, Guy, et autres
Publié: (2024)

Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
par: Lyu, Xiaoting, et autres
Publié: (2024)

Model Extraction Attacks Revisited
par: Liang, Jiacheng, et autres
Publié: (2023)