12 citations · 15 across the 9 of their papers we have counts for
10 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,…
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…
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…
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…
Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
Huifa Li, Jie Fu, Xinpeng Ling +3
The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contrib…
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…
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…
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Jie Fu, Qingqing Ye, Haibo Hu +4
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and member…
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering
Huifa Li, Jie Fu, Zhili Chen +3
Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-c…
Truncated Laplace and Gaussian mechanisms of RDP
Jie Fu, Zhiyu Sun, Haitao Liu +1
The Laplace mechanism and the Gaussian mechanism are primary mechanisms in differential privacy, widely applicable to many scenarios involving numerical data. However, due to the i…