Deep Neural Network Integration for Transparency and Security in Cloud-Native Healthcare IT Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806003Keywords:
Deep Neural Networks (DNN), Cloud-Native Healthcare Systems, Data Security, Transparency, Explainable AI (XAI), Zero-Trust Architecture, Blockchain, Interoperability, Privacy Preservation, Anomaly Detection, Federated Learning, Healthcare IT Infrastructure, Cybersecurity, HIPAA Compliance, AI GovernanceAbstract
The rapid digital transformation of healthcare has led to a growing dependence on cloud-native IT systems for managing sensitive medical data, analytics, and patient services. However, this evolution introduces challenges related to data security, privacy, and transparency across distributed environments. This paper proposes a Deep Neural Network (DNN)-integrated framework designed to enhance security, interpretability, and operational transparency in cloud-based healthcare ecosystems. The framework leverages DNN-based anomaly detection and encryption models to safeguard patient data, detect malicious activities, and ensure end-to-end traceability. It integrates AI-driven audit trails, blockchain-enabled access control, and zero-trust architecture principles within a multi-cloud infrastructure, enabling dynamic scalability and interoperability between healthcare entities. Furthermore, the system employs explainable AI (XAI) components to interpret DNN decisions, fostering regulatory compliance with standards such as HIPAA and GDPR. Experimental validation demonstrates significant improvements in data breach detection accuracy, reduced false positives, and increased processing efficiency when compared to traditional security models. The proposed approach contributes toward building transparent, secure, and accountable healthcare IT infrastructures, promoting trust and reliability in next-generation clinical and administrative systems.
References
1. Pati, S. (2024). Privacy preservation for federated learning in healthcare. Patterns.
2. Balaji, P. C., & Sugumar, R. (2025, April). Accurate thresholding of grayscale images using Mayfly algorithm comparison with Cuckoo search algorithm. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020114). AIP Publishing LLC.
3. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
4. Lin, T., Kukkadapu, S., & Suryadevara, G. (2025, March). A Cloud-Native Framework for Cross-Industry Demand Forecasting: Transferring Retail Intelligence to Manufacturing with Empirical Validation. In 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) (pp. 1115-1123). IEEE.
5. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
6. Teo, Z. L., et al. (2024). Federated machine learning in healthcare: A systematic review. PMC.
7. Reddy, B. T. K., & Sugumar, R. (2025, June). Effective forest fire detection by UAV image using Resnet 50 compared over Google Net. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020274). AIP Publishing LLC.
8. Adnan, M., et al. (2022). Federated learning and differential privacy for medical image analysis. PMC.
9. Oracle. (2025). AI-enhanced oracle platforms: A new era of predictive healthcare analytics. International Journal of Multidisciplinary Research and Growth Evaluation.
10. Oracle. (2025). Federated Learning Approach to Protect Healthcare Data over Big Data Scenario. ResearchGate.
11. Choudhury, A. (2025). Advancing Privacy-Preserving Healthcare Analytics. JMIR AI.
12. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
13. Teo, Z. L., et al. (2024). Federated Learning in Healthcare: Privacy-Preserving AI for Secure Medical Data Analysis. ResearchGate.
14. M. Krishnapatnam, "Cutting-Edge AI Techniques for Securing Healthcare IAM: A Novel Approach to SAML and OAuth Security," International Journal of Computing and Engineering, vol. 7, no. 2, pp. 39-50, 2025, doi: 10.47941/ijce.2630.
15. Arjunan, T. (2024). A comparative study of deep neural networks and support vector machines for unsupervised anomaly detection in cloud computing environments. International Journal for Research in Applied Science and Engineering Technology, 12(9), 10-22214.
16. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
17. Raju, L. H. V., & Sugumar, R. (2025, June). Improving jaccard and dice during cancerous skin segmentation with UNet approach compared to SegNet. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020271). AIP Publishing LLC.
18. Pati, S. (2024). Privacy Preservation for Federated Learning in Healthcare. Patterns.
19. Oracle. (2025). SymetryML: Deploy a Predictive, Federated Healthcare Solution. Oracle Documentation.
20. Sangannagari, S. R. (2024). Design and Implementation of a Cloud-Native Automated Certification Platform for Functional Testing and Compliance Validation. International Journal of Technology, Management and Humanities, 10(02), 34-43.
21. Venkata Ramana Reddy Bussu,, Sankar, Thambireddy, & Balamuralikrishnan Anbalagan. (2023). EVALUATING THE FINANCIAL VALUE OF RISE WITH SAP: TCO OPTIMIZATION AND ROI REALIZATION IN CLOUD ERP MIGRATION. International Journal of Engineering Technology Research & Management (IJETRM), 07(12), 446–457. https://doi.org/10.5281/zenodo.15725423
22. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2023). Navigating digital privacy and security effects on student financial behavior, academic performance, and well-being. Data Analytics and Artificial Intelligence, 3(2), 235–246.
