Multiparty Privacy-Preserving AI with SAP-Based Cyber Defense for Healthcare Business Processes in Cloud and 5G

Abstract

The rapid adoption of cloud computing, 5G networks, and data-driven automation in healthcare has intensified the need for secure, privacy-preserving, and compliant artificial intelligence (AI) frameworks. This paper proposes a multiparty privacy-preserving AI architecture integrated with SAP-enabled risk-based cyber defense to support secure healthcare cloud and 5G-enabled MLOps environments. The framework leverages federated learning, secure aggregation, and differential privacy to enable collaborative model training across distributed healthcare stakeholders while ensuring sensitive patient data remains protected. SAP security and risk management capabilities are incorporated to provide continuous threat intelligence, policy enforcement, and real-time risk assessment across cloud, network, and application layers. The proposed architecture addresses key challenges related to data confidentiality, regulatory compliance, adversarial attacks, and operational resilience in healthcare AI pipelines. By aligning privacy-preserving AI techniques with enterprise-grade SAP security analytics, the solution enhances trust, scalability, and auditability in multiparty healthcare ecosystems. The model demonstrates how secure MLOps can be achieved across heterogeneous cloud and 5G infrastructures while maintaining robust cyber defense and governance.