Privacy-Enhanced Federated Cloud Medical Data Management with Oracle AI, SAP, and Apache Security
DOI:
https://doi.org/10.15662/IJRAI.2025.0806812Keywords:
Federated cloud, Oracle AI, SAP healthcare systems, medical data privacy, Apache security tools, threat detection, healthcare data management, interoperable analyticsAbstract
The increasing digitization of healthcare demands secure, privacy-preserving, and interoperable systems capable of managing sensitive medical data across distributed environments. This study presents a privacy-driven medical data management framework operating within federated cloud environments and strengthened by Oracle AI, SAP healthcare systems, and Apache-based security tools. The proposed architecture enables seamless data integration, multi-site collaboration, and privacy-aware analytics using Oracle Machine Learning (OML), while SAP modules ensure clinical workflow interoperability and standardized data exchange. Apache tools—such as Kafka, Ranger, and Metron—enable continuous threat monitoring, secure access control, and real-time anomaly detection to safeguard patient information. The federated cloud model further enhances compliance with healthcare regulations by decentralizing data storage, minimizing exposure, and supporting secure cross-institutional analytics. The integrated system demonstrates improved data privacy, strengthened threat resilience, and efficient AI-powered clinical insights, establishing a robust blueprint for next-generation medical data management.
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