3 papers
cs.LG2024
Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation
He Zhang, Bang Wu, Xiangwen Yang +3
Dynamic graph neural networks (DGNNs) have emerged and been widely deployed in various web applications (e.g., Reddit) to serve users (e.g., personalized content delivery) due to t…
cs.CR2023
Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification
Bang Wu, Xingliang Yuan, Shuo Wang +3
The deployment of Graph Neural Networks (GNNs) within Machine Learning as a Service (MLaaS) has opened up new attack surfaces and an escalation in security concerns regarding model…
cs.LG2023
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
Bang Wu, He Zhang, Xiangwen Yang +4
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse…