Privacy-Preserving AI-Enabled Cloud Lakehouse Ecosystem using Blockchain and Machine Learning for Secure SAP Web Applications and Medical Imaging Analytics

Abstract

The rapid adoption of cloud-based data platforms and artificial intelligence (AI) has transformed enterprise applications and healthcare analytics. However, concerns related to data privacy, security, regulatory compliance, and interoperability remain significant challenges, particularly for sensitive domains such as SAP enterprise systems and medical imaging. This paper proposes a privacy-preserving AI-enabled cloud lakehouse ecosystem that integrates blockchain technology and machine learning to ensure secure, transparent, and compliant data management. The proposed architecture leverages blockchain for immutable audit trails, decentralized access control, and data provenance, while machine learning models enable intelligent analytics and automation. The lakehouse paradigm unifies structured and unstructured data processing, supporting real-time SAP web applications and large-scale medical imaging analytics. Privacy-enhancing techniques such as encryption, federated learning, and role-based access control are incorporated to meet regulatory requirements including HIPAA and GDPR. Experimental evaluation demonstrates improved data security, system scalability, and analytics performance compared to traditional cloud architectures. The proposed ecosystem provides a robust framework for secure digital transformation across enterprise and healthcare domains.