Privacy-Preserving Federated Learning Frameworks for Telematics Data in Auto Insurance Analytics
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P106Keywords:
Federated Learning, Telematics Data, Privacy Preservation, Auto Insurance Analytics, Differential PrivacyAbstract
In particular, the use of telematics data in auto insurance has dramatically changed risk underwriting and pricing but raises serious privacy issues for drivers sensitive information. In this work, we investigate privacy-preserving federated learning methods for fostering collaborative machine learning between multiple insurance providers that does not require centralization of telematics data. Our work explores use of differential privacy, homomorphic encryption, and secure aggregation to safeguard driver behavior patterns, location data and vehicular telemetry while preserving prediction accuracy. We conjecture that hybrid privacy approaches for federated learning architectures are capable of providing the same level of model performance as centralized ones guaranteeing a strong security and privacy level in data. By analyzing real-world deployment scenarios, we find that when using privacy-preserving frameworks the privacy leakage is reduced by 73-85%, while model accuracy remains above 91%. The work shows that homomorphic encryption based aggregation can provide better privacy with 8-12% computing overhead. These findings show that federated learning frameworks can be potential solutions to insurance telematics analytics, which allows for risk modeling in a collaborative way under data protection regulation and competitive intelligence through privacy-preserving collaboration mechanism
References
[1] Alabdulkarim, A., Al-Qurishi, M., Al-Rakhami, M. S., Alamri, A., Alrubaian, M., Hassan, M. M., & Fortino, G. (2023). Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm. Future Internet, 15(9), 310. https://doi.org/10.3390/fi15090310
[2] Bharati, S., Mondal, M. R., Podder, P., & Prasath, V. S. (2022). Federated learning: Applications, challenges and future directions. International Journal of Hybrid Intelligent Systems, 18(1-2), 19-35.
[3] Chen, X. (2024). Transforming auto insurance: The impact of telematics and real-time data on pricing and risk assessment. Regent Journal of Business and Technology, 1(1), 36-43. https://doi.org/10.5923/j.rjbt.20240101.03
[4] Drainakis, G., Pantazopoulos, P., Katsaros, K. V., Sourlas, V., Amditis, A., & Kaklamani, D. I. (2023). From centralized to federated learning: Exploring performance and end-to-end resource consumption. Computer Networks, 225, 109637.
[5] Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2021). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 3(4), 305-311. https://doi.org/10.1038/s42256-021-00337-8
[6] Khalid, I., Qayyum, F., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
[7] S. K. Gunda, "Automatic Software Vulnerabilty Detection Using Code Metrics and Feature Extraction," 2025 2nd International Conference On Multidisciplinary Research and Innovations in Engineering (MRIE), Gurugram, India, 2025, pp. 115-120, https://doi.org/10.1109/MRIE66930.2025.11156601.
[8] Li, Q., Wen, Z., Dai, C., Liu, J., Shuang, H., & Sun, C. (2019). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3347-3366.
[9] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273-1282). PMLR.
[10] Aleti AK. Reinforcement Learning Driven Adaptive Software Testing with Continuous Fault Anticipation and Self-Healing Feedback Loops in SAP. 2025 Oct. 19;6(4):24-31. https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P104
[11] Mohammadi, S., Norouzi, M., Mälardalen, L., & Khoshkholghi, M. A. (2024). Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics. Journal of Parallel and Distributed Computing, 190, 104883. https://doi.org/10.1016/j.jpdc.2024.104883
[12] S. K. Gunda, "Comparative Analysis of Machine Learning Models for Software Defect Prediction," 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, https://doi.org/10.1109/ICPECTS62210.2024.10780167.
[13] Nevrataki, T., Iliadou, A., Ntolkeras, G., Sfakianakis, I., Lazaridis, L., Maraslidis, G., Asimopoulos, N., & Fragulis, G. F. (2023). A survey on federated learning applications in healthcare, finance, and data privacy/data security. AIP Conference Proceedings, 2909(1), 120015. https://doi.org/10.1063/5.0182160
[14] Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022). Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge. IEEE Open Journal of the Computer Society, 3, 172-184. https://doi.org/10.1109/OJCS.2022.3206407
[15] S. K. Gunda, "Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison," 2024 Asian Conference on Intelligent Technologies (ACOIT), KOLAR, India, 2024, pp. 1-5, https://doi.org/10.1109/ACOIT62457.2024.10939610.
[16] Shiranthika, C., Saeedi, P., & Bajić, I. V. (2023). Decentralized learning in healthcare: A review of emerging techniques. IEEE Access, 11, 54188-54209. https://doi.org/10.1109/ACCESS.2023.3281832
[17] Krishna GV, Reddy BD, Vrindaa T. EmoVision: An Intelligent Deep Learning Framework for Emotion Understanding and Mental Wellness Assistance in Human Computer Interaction. 2025 Oct. 16;6(4):14-23. https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P103.
[18] Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2023). Beyond inferring class representatives: User-level privacy leakage from federated learning. In Proceedings of IEEE INFOCOM 2023 (pp. 1-10). IEEE. https://doi.org/10.1109/INFOCOM53939.2023.10228958
[19] S. K. Gunda, "A Deep Dive into Software Fault Prediction: Evaluating CNN and RNN Models," 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India, 2024, pp. 224-228, https://doi.org/10.1109/ICESIC61777.2024.10846549.
[20] Xiang, Z., Liu, Y., & Chen, X. (2025). Research on privacy protection technology in federated learning. In Proceedings of the 2025 International Conference on Data Intelligence and Security (pp. 136-142). SCITEPRESS.
[21] Gunda, S. K. (2025). Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations. In B. Ben Youssef & M. Ben Ismail (Eds.), Integrating Machine Learning Into HPC-Based Simulations and Analytics (pp. 229-252). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3795-7.ch009.
[22] I. Manga, "AutoML for All: Democratizing Machine Learning Model Building with Minimal Code Interfaces," 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2025, pp. 347-352, doi: 10.1109/ICSCDS65426.2025.11167529.
[23] S. R. Gudi, "Ensuring Secure and Compliant Fax Communication: Anomaly Detection and Encryption Strategies for Data in Transit," 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp. 786-791, https://doi.org/10.1109/ICIMIA67127.2025.11200537
[24] Gunda, S. K., Yalamati, S., Gudi, S. R., Manga, I., & Aleti, A. K. (2025). Scalable and adaptive machine learning models for early software fault prediction in agile development: Enhancing software reliability and sprint planning efficiency. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.74
[25] Srikanth Reddy Gudi. (2025). A Comparative Analysis of Pivotal Cloud Foundry and OpenShift Cloud Platforms. The American Journal of Applied Sciences, 7(07), 20–29. https://doi.org/10.37547/tajas/Volume07Issue07-03
[26] I. Manga, "Towards Explainable AI: A Framework for Interpretable Deep Learning in High-Stakes Domains," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1354-1360, doi: 10.1109/ICSCSA66339.2025.11170778.
[27] S. R. Gudi, "Monitoring and Deployment Optimization in Cloud-Native Systems: A Comparative Study Using OpenShift and Helm," 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp. 792-797, https://doi.org/10.1109/ICIMIA67127.2025.11200594
[28] I. Manga, "Federated Learning at Scale: A Privacy-Preserving Framework for Decentralized AI Training," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 110-115, doi: 10.1109/ICSCSA66339.2025.11170780.
[29] S. R. Gudi, "Deconstructing Monoliths: A Fault-Aware Transition to Microservices with Gateway Optimization using Spring Cloud," 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2025, pp. 815-820, https://doi.org/10.1109/ICESC65114.2025.11212326
[30] I. Manga, "Unified Data Engineering for Smart Mobility: Real-Time Integration of Traffic, Public Transport, and Environmental Data," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1348-1353, doi: 10.1109/ICSCSA66339.2025.11170800.
[31] Gudi, S. R. (2025). Enhancing optical character recognition (OCR) accuracy in healthcare prescription processing using artificial neural networks. European Journal of Artificial Intelligence and Machine Learning, 4(6). https://doi.org/10.24018/ejai.2025.4.6.79
[32] Grover, S. (2025). Comprehensive Software Test Strategies for Subscription-Based Applications and Payment Systems. Utilitas Mathematica , 122(1), 3127–3143. https://utilitasmathematica.com/index.php/Index/article/view/2630
[33] Sujeet Kumar Tiwari. (2024). The Future of Digital Retirement Solutions: A Study of Sustainability and Scalability in Financial Planning Tools. Journal of Computer Science and Technology Studies, 6(5), 229-245. https://doi.org/10.32996/jcsts.2024.6.5.19
[34] Ramachandran, S. (2025). Evaluating AI Responses: A Step-by-Step Approach for Test Automation. The Eastasouth Journal of Information System and Computer Science, 2(03), 381–390. https://doi.org/10.58812/esiscs.v2i03.540
[35] Jakkula, V. K. (2025). Design Pattern Usage in Large-Scale .NET Applications. International Journal of Engineering and Architecture, 2(2), 1–17. https://doi.org/10.58425/ijea.v2i2.420
[36] Malviya, S., & Vrushali Parate. (2025). AI-Augmented Data Quality Validation in P&C Insurance: A Hybrid Framework Using Large Language Models and Rule-Based Agents. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3613
[37] N. S. M. Vuppala, D. Gupta, and S. Yadav, “Securing Healthcare Transactions in AI-Augmented Systems: A comprehensive framework for enhanced cybersecurity in health insurance operations,” The American Journal of Applied Sciences, vol. 07, no. 10, pp. 44–51, Oct. 2025, doi: 10.37547/tajas/volume07issue10-04.
[38] Kishore Subramanya Hebbar. (2025). AI-DRIVEN REAL-TIME FRAUD DETECTION USING KAFKA STREAMS IN FINTECH. International Journal of Applied Mathematics, 38(6s), 770–782. https://doi.org/10.12732/ijam.v38i6s.433
[39] Jain, R., Sai Santosh Goud Bandari, & Naga Sai Mrunal Vuppala. (2025). Polynomial Regression Techniques in Insurance Claims Forecasting. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3519
[40] I. Manga, "Scalable Graph Neural Networks for Global Knowledge Representation and Reasoning," 2025 9th International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2025, pp. 1399-1404, doi: 10.1109/ICISC65841.2025.11188341.
