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Enhancing Health Information Systems Security: An Ontology Model Approach

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Health Information Science (HIS 2023)

Abstract

This study explores the implications of integrating Health Information System (HIS) on the security and privacy of sensitive patient information. It identifies existing gaps in research and proposes a novel security ontology model aimed at strengthening the defence of health information systems. The model revolves around the Ontology Conceptual Security Model, which comprehensively captures the intricate relationships between different components of HIS security. By incorporating elements such as Health Information, HIS Security Conditions, and Semantic Web Rule Language (SWRL) rules, the model promotes the establishment of rule-based access policies. It effectively combines various access control strategies, including Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Mandatory Access Control (MAC). This integration ensures both flexibility and compliance with regulatory requirements. While the model represents a significant advancement in the field, it recognizes the need for further validation and addresses future challenges. Specifically, it highlights the importance of exploring advanced access control mechanisms and seamless integration with existing systems. In essence, this study presents a comprehensive framework for a robust security ontology model designed to enhance the protection of patient data within HIS systems.

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Correspondence to Raza Nowrozy .

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Nowrozy, R., Ahmed, K. (2023). Enhancing Health Information Systems Security: An Ontology Model Approach. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_8

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  • DOI: https://doi.org/10.1007/978-981-99-7108-4_8

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