Semantically Enriched Data Access Policies in eHealth.

Area of Science:

• Computer Science
• Information Security
• Health Informatics

Background:

• The Internet of Things (IoT) necessitates advanced access control mechanisms for autonomous yet secure resource management.
• Interactions between diverse entities, including devices and humans, require flexible and robust access policies.
• eHealth applications face unique challenges in managing sensitive data access within IoT environments.

Purpose of the Study:

• To propose a novel approach for enhancing access control policies in eHealth IoT systems.
• To integrate semantic enrichment into existing eXtensible Access Control Markup Language (XACML) policies.
• To facilitate controlled and autonomous resource access for heterogeneous entities in eHealth.

Main Methods:

• Enriching standard eXtensible Access Control Markup Language (XACML) policies with semantic information.
• Implementing a Policy Information Point (PIP) as a core component of the proposed system.
• Integrating the PIP with the HL7 Security and Privacy Ontology to provide contextual understanding.

Main Results:

• Demonstration of a functional implementation of semantically enriched XACML policies.
• Successful integration of the Policy Information Point with the HL7 Security and Privacy Ontology.
• Validation of the approach's capability to manage access control in eHealth IoT scenarios.

Conclusions:

• Semantic enrichment of XACML policies offers a viable solution for complex access control in eHealth IoT.
• The proposed approach enhances the autonomy and security of resource access for diverse entities.
• Integration with established ontologies like HL7 facilitates more intelligent and context-aware access decisions.