CNN-Driven Healthcare Intelligence in Enterprise Cloud Platforms: Security and Privacy Considerations
Main Article Content
Abstract
Convolutional Neural Networks (CNNs) have become a foundational component of modern healthcare intelligence systems, particularly in medical imaging, disease diagnosis, and predictive analytics. When integrated into enterprise cloud platforms, CNN-driven solutions enable scalable data processing, real-time clinical insights, and cross-institutional collaboration. However, the sensitive nature of healthcare data introduces critical security and privacy challenges that must be addressed to ensure regulatory compliance, patient trust, and system integrity. This paper explores the intersection of CNN-based healthcare intelligence and enterprise cloud computing, with a particular focus on security and privacy considerations. It examines the architectural role of CNNs in healthcare analytics, the benefits and risks of cloud deployment, and the vulnerabilities introduced by distributed and multi-tenant environments. Key threats such as data breaches, model inversion attacks, unauthorized access, and compliance violations are analyzed. Furthermore, the paper reviews existing security frameworks, encryption techniques, access control mechanisms, and privacy-preserving machine learning approaches applicable to CNN-based healthcare systems. A comprehensive research methodology is proposed to evaluate secure deployment models, combining qualitative risk assessment and quantitative performance analysis. The study aims to provide practical insights for healthcare organizations and cloud service providers seeking to deploy intelligent, secure, and privacy-compliant CNN-driven healthcare systems at enterprise scale
Article Details
Section
How to Cite
References
1. Rajan, P. K. (2023). Predictive Caching in Mobile Streaming Applications using Machine Learning Models. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8737-8745.
2. Genne, S. (2022). Designing accessibility-first enterprise web platforms at scale. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7679–7690.
3. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
4. Surisetty, L. S. (2021). Zero-Trust Data Fabrics: A Policy-Driven Model for Secure Cross-Cloud Healthcare and Financial Data Exchanges. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(2), 4548-4556.
5. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
6. Panda, M. R., Devi, C., & Dhanorkar, T. (2024). Generative AI-Driven Simulation for Post-Merger Banking Data Integration. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 339-350.
7. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
8. Gopinathan, V. R. (2024). Secure Explainable AI on Databricks–SAP Cloud for Risk-Sensitive Healthcare Analytics and Swarm-Based QoS Control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
9. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
10. 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.
11. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
12. Zerine, I., Islam, M. S., Ahmad, M. Y., Islam, M. M., & Biswas, Y. A. (2023). AI-Driven Supply Chain Resilience: Integrating Reinforcement Learning and Predictive Analytics for Proactive Disruption Management. Business and Social Sciences, 1(1), 1-12.
13. Gangina, P. (2023). Service mesh implementation strategies for zero-downtime migrations in production environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7208–7220.
14. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7691–7702. https://doi.org/10.15662/IJRAI.2022.0505007
15. Sriramoju, S. (2024). Optimizing data flow: A unified approach for product, pricing, and revenue sync in enterprise systems. International Journal of Engineering & Extended Technologies Research, 6(1), 7492–7503.
16. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
17. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943-948). IEEE.
18. Ponugoti, M. (2022). Integrating full-stack development with regulatory compliance in enterprise systems architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(2), 6550–6563.
19. Natta, P. K. (2024). Closed-loop AI frameworks for real-time decision intelligence in enterprise environments. International Journal of Humanities and Information Technology, 6(3). https://doi.org/10.21590/ijhit.06.03.05
20. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
21. Kusumba, S. (2024). Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making. The Eastasouth Journal of Information System and Computer Science, 2(02), 189-208.
22. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
23. Ramidi, M. (2023). Implementing privacy-focused data sharing frameworks for mobile healthcare communication. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8746–8757.
24. Alam, M. K., Mahmud, M. A., & Islam, M. S. (2024). The AI-Powered Treasury: A Data-Driven Approach to managing America’s Fiscal Future. Journal of Computer Science and Technology Studies, 6(2), 236-256.
25. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY-PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(4), 3400-3405.
26. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.
27. Kota, R. K., Keezhadath, A. A., & Kondaveeti, D. (2021). AI-Driven Predictive Analytics in Retail: Enhancing Customer Engagement and Revenue Growth. American Journal of Autonomous Systems and Robotics Engineering, 1, 234-274.
28. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
29. Chennamsetty, C. S. (2022). Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(5), 7121-7133.
30. 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.
31. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12-24.
32. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
33. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49-63.
34. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.