1 citations · 1 across the 10 of their papers we have counts for
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PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks
Sindhuja Madabushi, Haider Ali, Ahmad Faraz Khan +5
Vertical Federated Learning (VFL) enables collaborative model training across organizations that share common user samples but hold disjoint feature spaces. Despite its potential,…
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
Jiaqi Xue, Mayank Kumar, Yuzhang Shang +5
Federated Learning (FL) enables collaborative model training across institutions without sharing raw data. However, gradient sharing still risks privacy leakage, such as gradient i…
Heterogeneous Graph Backdoor Attack
Jiawei Chen, Lusi Li, Daniel Takabi +2
Heterogeneous Graph Neural Networks (HGNNs) excel in modeling complex, multi-typed relationships across diverse domains, yet their vulnerability to backdoor attacks remains unexplo…