Privacy-Preserving Health Data Transmission in IoT-Enabled Mobile Health Systems

The Internet of Things (IoT)-enabled mobile health systems serve a vital function in supporting real-time health monitoring by capturing, transmitting, and processing sensitive patient data. Ensuring the privacy and integrity of this data during transmission remains a core challenge, especially in resourceconstrained environments. By applying secure data clustering mechanisms, researchers can efficiently manage health information flow while preserving patient confidentiality. This paper proposes a novel framework for secure data transmission leveraging the Privacy-Preserving Clustered Encryption Algorithm (PPCEA). Through a combination of cluster-based segmentation and layered encryption, the method enables classified grouping of health data according to sensitivity level while maintaining data anonymity during transmission. The PPCEA facilitates secure routing of health information collected from wearable IoT devices, classifying transmissions into privacy-preserved clusters. For example, Cluster A handles high-sensitivity biometric data with advanced encryption, while Cluster B processes generalized health metrics under lightweight encryption protocols. Transmission efficiency and privacy assurance were evaluated, revealing transmission success rates above 97%, while privacy leakage risk was minimized to below 1.5%. The experimental outcomes demonstrate the capability of PPCEA to balance low-latency communication with robust privacy protection, making it suitable for deployment in mobile health systems within developing regions. These findings underscore the importance of clusteraware encryption techniques in enhancing data security without sacrificing communication efficiency.