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Designing secure data pipelines for medical billing fraud detection using homomorphic encryption and federated learning
1 Business Analytics Program (MSBA), University of Louisville, Kentucky, USA.
2 Department of Mathematics, University of Lagos, Akoka, Lagos State, Nigeria.
Research Article
International Journal of Science Research Archive, 2023, 10(02), 1210–1222.
Article DOI: 10.30574/ijsra.2023.10.2.0866
Publication history:
Received on 29 September 2023; revised on 07 November 2023; accepted on 09 November 2023
Abstract:
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.
Keywords:
Medical billing fraud; Homomorphic encryption; Federated learning; Differential privacy; Healthcare data security
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Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
