Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Deep graph learning has achieved remarkable progresses in both business and scientific
areas ranging from finance and e-commerce, to drug and advanced material discovery …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows
multiple clients to jointly train a model without sharing their private data. Recently, many …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …

Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive
representation from knowledge graph (KG) to facilitate diverse downstream tasks. The …

Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its
privacy implications intensifies. Despite a growing corpus of research in AI privacy and …