CFDGraph: Privacy-Preserving Graph Processing for Large-Scale Collaborative Fraud Detection

Fraud detection is a critical task in finance and e-commerce, but fraudsters increasingly evade detection by distributing their activities across multiple institutions. While this makes collaborative fraud detection (CFD) essential, it is hindered by two fundamental barriers: stringent data privacy regulations and constrained communication networks. Existing systems are impractical, as they either incur prohibitive overheads or cannot attain the high detection accuracy required to manage financial risk. This paper introduces CFDGraph, the first privacy-preserving graph processing system designed to overcome these challenges by synergistically balancing accuracy, privacy, and communication efficiency. CFDGraph's novelty lies in three interconnected innovations. First, it employs differential privacy with adaptive quantization to simultaneously reduce both privacy-induced noise and communication overhead. Second, it introduces accuracy-aware noise mitigation techniques to preserve the high recall essential for fraud detection. Third, it implements privacy-aware message compression to dramatically reduce data transmission volume. Evaluations show CFDGraph achieves 100% recall in identifying top-k fraudulent accounts while reducing manual investigation costs by up to 99.5% compared to state-of-the-art approaches.