28 citations · 28 across the 4 of their papers we have counts for
4 papers
VIRGOS: Secure Graph Convolutional Network on Vertically Split Data from Sparse Matrix Decomposition
Yu Zheng, Qizhi Zhang, Lichun Li +2
Securely computing graph convolutional networks (GCNs) is critical for applying their analytical capabilities to privacy-sensitive data like social/credit networks. Multiplying a s…
Revisiting Privacy-Utility Trade-off for DP Training with Pre-existing Knowledge
Yu Zheng, Wenchao Zhang, Yonggang Zhang +5
Differential privacy (DP) provides a provable framework for protecting individuals by customizing a random mechanism over a privacy-sensitive dataset. Deep learning models have dem…
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
Xing Ai, Guanyu Zhu, Yulin Zhu +4
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding gene…
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Yu Zheng +4
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data…