Privacy-Preserving Fraud Detection via Blockchain and Machine Learning: A Decentralized and Incentive-Driven Framework

Detecting fraud in digital environments presents a persistent challenge, particularly where the need for real-time, large-scale data analysis conflicts with stringent privacy requirements. This paper introduces a novel framework that synergizes blockchain technology with advanced machine learning techniques to achieve secure, privacy-preserving, and collaborative fraud detection. The architecture employs blockchain to ensure data integrity, transparency, and decentralized trust, while integrating federated learning and differential privacy to train models without exposing sensitive user information. To incentivize participation, the system incorporates a dynamic, smart contract-based reward mechanism that encourages the contribution of high-quality data. By uniting privacy-aware computation with decentralized infrastructure, the proposed solution offers robust fraud detection capabilities, protects user confidentiality, and fosters cross-organizational cooperation. Experimental results on both synthetic and real-world financial datasets demonstrate the framework’s effectiveness, scalability, and adaptability in detecting sophisticated and evolving fraud patterns.