Privacy-Preserving Graph Processing for Cross-Institional Fraud Detection

This project aims to create a distributed graph computing privacy protection model for cross-institutional scenarios. It seeks to design data privacy methods that balance system performance and result usability, ensuring secure and efficient operations. To accomplish these goals, the project proposes adaptive differential privacy K-means quantization, message combining and clipping techniques, and vertex reindexing and message aggregation methods.