Privacy-Preserving Healthcare Intelligence Systems with Cloud-Native Predictive Analytics and Secure Automation
DOI:
https://doi.org/10.15662/IJEETR.2025.0706043Keywords:
Privacy-preserving computing, healthcare intelligence, cloud-native architecture, predictive analytics, federated learning, homomorphic encryption, differential privacy, secure automation, EHR, machine learningAbstract
The rapid digitization of healthcare systems has enabled unprecedented data generation from electronic health records (EHRs), wearable devices, medical imaging systems, and IoT-enabled monitoring tools. While this data offers immense potential for predictive analytics and intelligent decision-making, it raises critical concerns regarding patient privacy, data security, and regulatory compliance. This paper proposes a privacy-preserving healthcare intelligence system built on cloud-native architecture integrated with secure automation and predictive analytics capabilities. The system leverages advanced cryptographic techniques such as homomorphic encryption, federated learning, and differential privacy to ensure sensitive medical data remains protected throughout its lifecycle. Cloud-native technologies, including microservices, containerization, and Kubernetes orchestration, enable scalability, resilience, and real-time processing of healthcare workloads. Predictive analytics models powered by machine learning are embedded to support early disease detection, patient risk stratification, and operational optimization in healthcare delivery. Additionally, secure automation workflows using policy-driven access control and AI-assisted decision systems enhance efficiency while maintaining compliance with healthcare regulations such as HIPAA and GDPR. The proposed framework demonstrates how privacy-preserving computation and cloud-native intelligence can coexist to transform healthcare systems into secure, adaptive, and predictive ecosystems. The study highlights architectural design, implementation strategies, and performance considerations for deploying such systems in real-world healthcare environments.
References
1. Patel, M., & Chaturvedi, V. (2025). A survey on artificial intelligence techniques for disease prediction in healthcare. ESP Journal of Engineering & Technology Advancements, 5(4), 201–210.
2. Adepu, G. (2025). AI-based epidemiological data platforms for early outbreak detection and real-time health analytics. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 9–29.
3. Myakala, P. K., & Naayini, P. (2023). Bridging the Gap: Leveraging Transfer Learning for Low-Resource NLP Tasks. International Journal of Computer Techniques, 10(5).
4. Guda, D. P. (2024). Cyber insurance for DevSecOps risks: Pricing models and coverage gaps. Journal of Information Systems Engineering and Management, 9(3).
5. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
6. Bellundagi, M. (2023). Blockchain-Based Secure Data Sharing Framework for Smart Applications. International Journal of Future Innovative Science and Technology (IJFIST), 6(2), 10268.
7. Gentyala, R. (2024). From features to financial personas: Mapping feature transformation efficacy to customer archetypes in behavioral banking data. International Journal of Computer Science and Engineering Research and Development, 14(1), 127-145.
8. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
9. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
10. Karvannan, R. (2024). Integrating Cloud Security and Healthcare Compliance in Pharmaceutical Operations. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10634-10641.
11. Rahman, M. B., Yasin, M., & Ahmed, M. P. (2024). Data-Driven Population Health Analytics for Identifying High-Risk Groups and Health Disparities. American Journal Of Botany And Bioengineering, 1(11), 58-82.
12. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
13. Narayanan, S. (2024). Cyber risk orchestration for systemic financial stability: An autonomous financial impact forecasting. International Journal of Research in Computer Applications and Information Technology, 7(2), 2927–2939. https://philarchive.org/archive/NARCRO
14. Kumar, S. A., & Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 19(11), 3841-3855.
15. Rahman, M. W., & Hossain, M. S. (2023). Integrating Generative AI into Business Analytics for Automated Strategic Insights. Integrating Generative AI into Business Analytics for Automated Strategic Insights, 6(12), 189-219.
16. Yamsani, N. (2016). Designing enterprise-wide reference data foundations for consistency, control, and operational integrity across complex institutional environments. International Journal of Scientific Research & Engineering Trends, 2(5). https://doi.org/10.5281/zenodo.18296676
17. Mudusu, S. K. (2025). AI-driven data engineering in the Internet of Things: Scaling data pipelines for smart device ecosystems. ISCSITR-International Journal of Data Engineering (ISCSITR-IJDE), 6(1), 1–9.
18. Parupalli, A. (2022). KPI-Driven Business Intelligence: A Review of Frameworks and Visualization Tools. Asian Journal of Computer Science Engineering, 7(4), 4.
19. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
20. Soundappan, S. J. (2022). AI-based fault detection and isolation for reliability in modern power systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
21. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
22. Adepu, R. (2024). Secure cloud migration strategies for enterprise data center modernization. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 239–258.
23. Sarabu, V. B. (2024). Architecting controlled international platform rollouts: Data governance, validation, and risk mitigation in retail modernization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 306–328.
24. Kasireddy, J. R. (2025). The transformative role of AI and machine learning in financial risk analysis. World Journal of Advanced Research and Reviews, 26(1), 1246–1256. https://doi.org/10.30574/wjarr.2025.26.1.1177
25. Appani, C. (2024). Explainable AI for fraud detection in financial transactions. Journal of Information Systems Engineering and Management, 9(3). https://jisem-journal.com/download/32_Explainable_AI_for_Fraud_Detection.pdf
26. Mallireddy, S. (2024). Trusting ServiceNow AI to deliver business value. International Journal of Research and Applied Innovations (IJRAI), 7(5), 55–58.
27. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., & Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.
28. Soujanya, T., Alsalami, Z., Srinath, S., Sengupta, J., & Das, A. (2024, May). Rooftop Photovoltaic Panel Segmentation using Improved Mask Region-based Convolutional Neural Network. In 2024 Second International Conference on Data Science and Information System (ICDSIS) (pp. 1-4). IEEE.
29. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.
30. Pandi Prabha, S., & Rengarajan, A. (2025, February). Decentralized Resource Allocation Model Using Multi-agent Reinforcement Learning for Cloud Environment. In International Conference on Universal Threats in Expert Applications and Solutions (pp. 71-82). Singapore: Springer Nature Singapore.
31. Aashiq Banu, S., Rao, L. K., Priya, P. S., Thanikaiselvan, Hemalatha, M., Dhivya, R., & Rengarajan, A. (2025). A review of genome to chaos: exploring DNA dynamics in security. Multimedia Tools and Applications, 84(22), 24859-24886.
32. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
33. Kunadi, S. K. (2021). Establishing robust data foundations: Early-stage architecture for scalable data warehousing and analytics systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3078–3088.
34. Suddala, V. R. A. K. (2025). Building scalable, secure, and compliance-ready healthcare e-commerce platforms in regulated environment. International Journal of Research and Applied Innovations, 8(4), 12699–12710.
35. Mali, R. K. (2023). A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8382-8391.
36. Nallamothu, T. K. (2023). Generative AI in healthcare: Automating clinical documentation, diagnostics, and knowledge synthesis. International Journal of Computer Technology and Electronics Communication, 6(1), 6376–6392.
37. Sharma, K. P., Kumar, I., Singh, P. P., Anbazhagan, K., Albarakati, H. M., Bhatt, M. W., ... & Rana, A. (2024). Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles. Computers in Human Behavior, 153, 108110.
38. Lanka, S. (2024). Redefining Digital Banking: ANZ’s Pioneering Expansion into Multi-Wallet Ecosystems. International Journal of Technology, Management and Humanities, 10(01), 33-41.
39. Kaliappan, S., Ragunthar, T., Ali, M., & Murugeshwari, B. (2024). Implementation of Virtual High Speed Data Transfer in Satellite Communication Systems Using PLC and Cloud Computing. In AI Approaches to Smart and Sustainable Power Systems (pp. 274-286). IGI Global Scientific Publishing.
40. Dave, B. L. (2024). Future-proof living leading a better life with artificial intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11233–11242.
41. Gopinathan, V. R. (2025). Intelligent workload scheduling for telecom cloud architecture using reinforcement learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13244-13255.
42. Boddupally, H. L. (2024). Embedding Governance into LLM Workflow Architectures for Enterprise-Wide Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(7), 279-294.
