Internet of Things (IoT) technology has revolutionized patient monitoring systems by providing real-time health data for better medical care. However, the rise of IoT devices raises privacy concerns about sensitive health data. This study uses Attribute-based Cryptography (ABC) in patient monitoring to address these concerns. The focus is on adapting attribute detail to IoT device features. Conventional encryption methods often struggle to adapt to IoT devices limited resources, which could compromise patient data privacy. This study suggests a tweak to ABC by adjusting the attribute granularity to better fit the limitations of IoT devices. These resource constraints present challenges, but the methodology proposes an innovative approach to handling and analyzing attributes while protecting patient confidentiality. A dynamic attribute granularity(DAG) model that adapts to IoT device capabilities ensures a data privacy-system performance trade-off in the study. The suggested approach optimizes attribute number and complexity to improve privacy-preserving patient monitoring system scalability and performance. The research thoroughly evaluates the modified ABC-DAG systems data privacy, scalability, and energy efficiency. The results show that the system can protect patient data in a healthcare IoT setting while reducing computational load on low-resource devices. The growing field of privacy-preserving healthcare Internet of Things benefits from this research. It addresses IoT attribute granularity challenges with a customized cryptographic method. The results improve ABC theory in healthcare and provide practical advice for secure and efficient IoT patient monitoring systems.