6 papers
Protecting K-Nearest Neighbor Queries from Location Inference Attacks
Zhiyu Sun, Jie Fu, Xinpeng Ling +2
The k-nearest neighbor query (kNNQ) is a core component of modern location-based services (LBS) and has been widely adopted in popular features such as ``people nearby''. However,…
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
Xinpeng Ling, Jie Fu, Kuncan Wang +3
Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persiste…
Safeguarding Graph Neural Networks against Topology Inference Attacks
Jie Fu, Yuan Hong, Zhili Chen +1
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, their widespread adoption has raised serious privacy concerns. While…
Differentially Private Federated Learning: A Systematic Review
Jie Fu, Yuan Hong, Xinpeng Ling +6
In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de…
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
Tong Cheng, Jie Fu, Xinpeng Ling +4
Graph Neural Networks (GNNs) have been widely used for graph analysis. Federated Graph Learning (FGL) is an emerging learning framework to collaboratively train graph data from var…
scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
Huifa Li, Jie Fu, Xinlin Zhuang +6
Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due…