Cover Image

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qiu, Pengyu, Zhang, Xuhong, Ji, Shouling, Fu, Chong, Yang, Xing, Wang, Ting
Format:	Preprint
Published:	2022
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

PackVFL: Efficient HE Packing for Vertical Federated Learning
by: Yang, Liu, et al.
Published: (2024)

Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)

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

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)

URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
by: Yao, Duanyi, et al.
Published: (2024)

UIFV: Data Reconstruction Attack in Vertical Federated Learning
by: Yang, Jirui, 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)

Defending Against Poisoning Attacks in Federated Learning with Blockchain
by: Dong, Nanqing, et al.
Published: (2023)

Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
by: He, Ping, et al.
Published: (2025)

FL-Defender: Combating Targeted Attacks in Federated Learning
by: Jebreel, Najeeb, et al.
Published: (2022)

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

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

GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending Against Prompt Injection Attacks
by: Li, Rongchang, et al.
Published: (2024)

Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)

SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark
by: Wen, Rui, et al.
Published: (2025)

CleanStack: A New Dual-Stack for Defending Against Stack-Based Memory Corruption Attacks
by: Chong, Lei
Published: (2025)

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis
by: Kawasaki, Amelia, et al.
Published: (2024)

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

Defending Against Indirect Prompt Injection Attacks With Spotlighting
by: Hines, Keegan, et al.
Published: (2024)

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

MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
by: Li, Jiacheng, et al.
Published: (2023)

Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization
by: Trillos, Nicolás García, et al.
Published: (2024)

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

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

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
by: Halloran, John T., et al.
Published: (2026)

Defending against Data Poisoning Attacks in Federated Learning via User Elimination
by: Galanis, Nick
Published: (2024)

Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE
by: Liu, Xinpeng, et al.
Published: (2025)

Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learning
by: Obioma, Chibueze Peace, et al.
Published: (2025)

FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
by: Wu, Di, et al.
Published: (2025)

A New Federated Learning Framework Against Gradient Inversion Attacks
by: Guo, Pengxin, et al.
Published: (2024)

From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks
by: Zhang, Zhexin, et al.
Published: (2024)

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

Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024)

Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
by: Zhang, Jiale, et al.
Published: (2025)

CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
by: Zeng, Rui, et al.
Published: (2024)

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

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025)

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

Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
by: An, Li, et al.
Published: (2025)