Privacy-Aware Machine Learning and Generative AI for Healthcare Data Security using SAP and Databricks
Abstract
The rapid adoption of cloud-native platforms in healthcare has intensified the need for robust data security and privacy-preserving analytics. This study presents a privacy-aware machine learning and generative AI framework for securing healthcare data using SAP-integrated Databricks platforms. The proposed approach leverages scalable lakehouse architecture, advanced machine learning pipelines, and generative AI techniques to enable secure data processing, real-time analytics, and intelligent decision support while maintaining regulatory compliance. Privacy-aware mechanisms such as secure data isolation, access control, and governance are incorporated to protect sensitive patient information across distributed environments. By integrating SAP systems with Databricks, the framework ensures interoperability, performance optimization, and enterprise-grade security for healthcare applications. Experimental observations demonstrate improved data security, scalability, and analytical efficiency, making the proposed architecture suitable for modern, cloud-native healthcare ecosystems.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17707-17714 |
Published |
December 23, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Christophe Julien Gauthier (2025). Privacy-Aware Machine Learning and Generative AI for Healthcare Data Security using SAP and Databricks. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17707-17714. https://doi.org/10.15662/IJAESIT.2025.0806003 |
References
2. Bonawitz, K., et al. (2017). Practical secure aggregation for federated learning on user-held data. Proceedings of the 2017 ACM SIGSAC Conference.
3. Patnaik, S. K., Sidhu, M. S., Gehlot, Y., Sharma, B., & Muthu, P. (2018). Automated skin disease identification using deep learning algorithm. Biomedical & Pharmacology Journal, 11(3), 1429.
4. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. STOC.
5. McMahan, B., et al. (2017). Communication-efficient learning of deep networks from decentralized data. AISTATS.
6. Bonawitz, K., et al. (2019). Towards federated learning at scale. SysML.
7. Pimpale, S. (2025). A Comprehensive Study on Cyber Attack Vectors in EV Traction Power Electronics. arXiv preprint arXiv:2511.16399.
8. Kesavan, E. (2023). ML-Based Detection of Credit Card Fraud Using Synthetic Minority Oversampling. International Journal of Innovations in Science, Engineering And Management, 55-62.
9. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
10. Chaudhari, B. B., Kabade, S., & Sharma, A. (2025, May). Leveraging AI to Strengthen Cloud Security for Financial Institutions with Blockchain-Based Secure E-Banking Payment System. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1490-1496). IEEE.
11. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.
12. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.
13. Cherukuri, B. R. (2025). Enhanced trimodal emotion recognition using multibranch fusion attention with epistemic neural networks and Fire Hawk optimization. Journal of Machine and Computer, 58, Article 202505005. https://doi.org/10.53759/7669/jmc202505005
14. Panda, M. R., & Chinthalapelly, P. R. (2023). Banking Sandbox Evaluation for Open Banking Ecosystems Using Agent-Based Modeling. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.
15. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.
16. Madabathula, L. (2022). Event-driven BI pipelines for operational intelligence in Industry 4.0. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6759–6769. https://doi.org/10.15662/IJRAI.2022.0502005
17. Singh, A. (2024). Integration of AI in network management. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11073–11078. https://doi.org/10.15662/IJRAI.2024.0704008
18. Manda, P. (2023). A Comprehensive Guide to Migrating Oracle Databases to the Cloud: Ensuring Minimal Downtime, Maximizing Performance, and Overcoming Common Challenges. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8201-8209.
19. Kumar, R. (2024). Real-Time GenAI Neural LDDR Optimization on Secure Apache–SAP HANA Cloud for Clinical and Risk Intelligence. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8737-8743.
20. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348-1353). IEEE.
21. 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
22. 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.
23. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136
24. Kavuru, Lakshmi Triveni. (2024). Cross-Platform Project Reality: Managing Work When Teams Refuse to use the Same Tool. International Journal of Multidisciplinary Research in Science Engineering and Technology. 10.15680/IJMRSET.2024.0706146.
25. Chivukula, V. (2020). Use of multiparty computation for measurement of ad performance without exchange of personally identifiable information (PII). International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1546–1551.
26. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
27. Karnam, A. (2024). Engineering Trust at Scale: How Proactive Governance and Operational Health Reviews Achieved Zero Service Credits for Mission-Critical SAP Customers. International Journal of Humanities and Information Technology, 6(4), 60–67. https://doi.org/10.21590/ijhit.06.04.11
28. Khan, M. I. (2025). Big Data Driven Cyber Threat Intelligence Framework for US Critical Infrastructure Protection. Asian Journal of Research in Computer Science, 18(12), 42-54.
29. 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.
30. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
31. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
32. Mahajan, N. (2024). AI-Enabled Risk Detection and Compliance Governance in Fintech Portfolio Operations. Cuestiones de Fisioterapia, 53(03), 5366-5381.
33. Borra, C. R. (2022). A Comparative Study of Privacy Policies in E-Commerce Platforms. International Journal of Research and Applied Innovations, 5(3), 7065-7069.
34. Kasireddy, J. R. (2023). A systematic framework for experiment tracking and model promotion in enterprise MLOps using MLflow and Databricks. International Journal of Research and Applied Innovations, 6(1), 8306–8315. https://doi.org/10.15662/IJRAI.2023.0601006
35. Rahman, M. R., Rahman, M., Rasul, I., Arif, M. H., Alim, M. A., Hossen, M. S., & Bhuiyan, T. (2024). Lightweight Machine Learning Models for Real-Time Ransomware Detection on Resource-Constrained Devices. Journal of Information Communication Technologies and Robotic Applications, 15(1), 17-23.
36. Ramalingam, S., Mittal, S., Karunakaran, S., Shah, J., Priya, B., & Roy, A. (2025, May). Integrating Tableau for Dynamic Reporting in Large-Scale Data Warehousing. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 664-669). IEEE.
37. 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.
38. Li, T., et al. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine.