A Structured Knowledge Model Linking Healthcare Privacy Risks to Privacy-Enhancing Technologies for AI-Enabled Health Systems

Increased applications of AI in healthcare has simultaneously raised concerns for privacy, security and regulatory compliance. Health data exchange due to its sensitive nature in context of patient privacy poses challenges that need to be addressed. Therefore, many privacy preserving and privacy enhancing techniques have been proposed and applied to address the various risks associated with patient privacy. these include Homomorphic Encryption, Trusted Execution Environments (TEEs), Differential Privacy, Federated Learning, ZeroKnowledge Proofs, Secure Multi-Party Computation (SMPC). However, while these techniques offer strong guarantees and mature methodologies, there exists a gap in guiding software practitioners and architects for applying appropriate Privacy Enhancing Techniques (PET) to their specific healthcare privacy risk scenario at a given AI workflow stage. This paper presents a structured knowledge model that maps healthcare privacy risk scenarios to appropriate privacy enhancing technologies for AI enabled systems. We present a taxonomy of privacy risks derived from existing literature and analyse the capabilities and limitations of available Privacy Enhancing techniques and integrate them into a structured conceptual framework. We then synthesize these findings into a set of governance-aware architectural patterns that systematically integrate PETs into AI pipelines to support privacy-by-design, accountability, and regulatory compliance. The proposed synthesis bridges the gap between theoretical PET research and practical AI system design by providing reusable architectural guidance rather than isolated technical solutions. The proposed framework enables design-stage privacy requirement assessment for AI enabled healthcare systems. This conceptual framework aims to guide future work on addressing privacy concerns when using AI enabled healthcare technologies.