Improving Patient Data Security and Privacy in Mobile Health Care: A Structure Employing WBANs, Multi-Biometric Key Creation, and Dynamic Metadata Rebuilding

Abstract

Rapid technological advancements in mobile health (m-health) have transformed healthcare 
services, increasing their efficacy and efficiency. However, there are concerns to patient data 
security and privacy when cloud computing and m-health are combined. In order to overcome 
these obstacles, this study suggests a safe framework for mobile healthcare that makes use of 
Wireless Body Area Networks (WBANs) and a multi-biometric key generation technique. For 
scalable data processing and storage, the framework makes use of cloud computing platforms, 
guaranteeing dependability and flexibility. The Discrete Wavelet Transform (DWT) is used to 
extract features from electroencephalogram (EEG) and electrocardiogram (ECG) signals, which 
aid in key generation and improve security. Furthermore, hospital community clouds' electronic 
medical records (EMRs) are protected by dynamic metadata reconstruction, which guarantees 
legal compliance with privacy regulations. With regard to inter-sensor communication and cloud 
storage privacy, the suggested framework fills the vacuum in the literature by offering extensive 
security safeguards. The framework prevents risks related to data transmission and storage by 
providing end-to-end patient information protection using a mix of dynamic metadata 
reconstruction and multi-biometric key creation. By providing a solution that not only enhances 
the functionality of mobile healthcare services but also protects sensitive patient data, the study 
underscores the urgent need for increased security in the convergence of m-health and cloud 
computing, supporting the creation of safe and effective healthcare delivery systems.