ABSTRACT Access control is a critical component of data protection, determining which individuals or systems are permitted to reach particular resources. Its purpose is to safeguard confidential information from unauthorized use, breaches, or unintended disclosure. In general computing environments, access control is relatively straightforward, but in complex domains such as healthcare, its implementation becomes significantly more challenging. The widespread adoption of Internet of Things solutions in medical systems, referred to as Healthcare IoT (H‐IoT), has introduced new layers of complexity. H‐IoT ecosystems combine medical devices, sensors, mobile health applications, and cloud platforms to continuously collect, transmit, and analyze patient data. In such dynamic, data‐intensive environments, traditional access control approaches often fall short. The study explores the application of access control within H‐IoT systems, analyzing established models including RBAC, ABAC, CapBAC, and CAAC, and discussing the benefits and limitations associated with each framework. The study addresses key healthcare‐specific challenges, including emergency access management and privacy compliance, and explores unified, adaptive, patient‐centric architectures that integrate roles, attributes, capabilities, and contextual factors. Additionally, it reviews emerging approaches such as predictive, machine learning‐driven mechanisms, decentralized enforcement at the edge or fog layer, and privacy‐preserving techniques aimed at enhancing responsiveness, scalability, and accountability. The paper concludes by identifying gaps in current research and suggesting directions for the development of H‐IoT systems that are adaptive, secure, and ethically responsible.
Nazir et al. (Wed,) studied this question.
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