Designing secure data pipelines for medical billing fraud detection using homomorphic encryption and federated learning

Medical billing fraud imposes significant financial and operational challenges on healthcare systems, highlighting the need for robust, privacy-preserving fraud detection solutions. This study presents a secure data pipeline that integrates homomorphic encryption (HE) and federated learning (FL) to enable decentralized fraud detection while maintaining patient confidentiality. Homomorphic encryption ensures data remains protected throughout the analytical process, while federated learning facilitates collaborative model training across healthcare institutions without requiring data centralization. Key findings reveal that increasing privacy levels via differential privacy effectively reduces data leakage risks, though it introduces minor computational overhead and a slight reduction in model accuracy. Scalability tests show that larger datasets considerably increase encryption time and memory usage, underscoring the need for optimized encryption algorithms. Additionally, secure communication protocols, while essential for data integrity, result in increased latency, which may impact real-time detection capabilities. The proposed pipeline achieves a balance between security and fraud detection accuracy, demonstrating its potential for real-world applications. However, further optimization of encryption methods and secure communication protocols is essential for broader scalability. This work advances privacy-centric approaches in healthcare fraud detection, setting a foundation for developing secure, scalable fraud detection systems.