## 1 Introduction  
The rapid proliferation of mobile health equipment and technologies has changed the patient's monitoring, distance diagnosis and PHR operations. As healthcare systems rapidly integrate wearables, smartphones, and cloud-connected infrastructure into daily workflows, the associated cybersecurity landscape becomes increasingly complex, introducing new risks that affect both medical environments and personal life alike.  
Wearable technologies such as smartwatches continuously track sensitive biometric data, including oxygen saturation and sleep cycle, which, if compromised, can pose significant threats to patient safety and system integrity. Recent analyses suggest that weaknesses often stem from the supply chain and firmware design of these devices, making them a potential entry point for widespread exploitation. For example, commonly used BLE enables spontaneous communication between wearables and smartphones, but can also expose sensitive metadata if not sufficiently rigid [1, 2, 3].  
Parallel to the wearable revolution, healthcare systems are increasingly adopting containerized architectures and microservices to enhance scalability and interoperability. However, these contemporary deployments are often insecure due to misconfigurations, poorly isolated services and outdated libraries. The literature suggests that hybrid cloud-edge deployments could offer a balanced approach between latency, analytics, and security [4,5].  
In the data layer, access to PHRs is a major concern. Although characteristics such as attribute-based encryption (ABE) and multi-committee encryption provide theoretical protection, they are rarely deployed in the real world of mHealth applications due to complexity and lack of performance. On the mobile side, platforms like Android, due to their open nature, have become attractive targets for both pre-installed and third-party applications. Studies report that many health-related apps exploit permissions, operate persistent background services, and collect user data without transparency [6, 7, 8].  
To address these challenges, this paper introduces a pragmatic, multi-layered security audit of the PHGL-COVID platform, a prototype mHealth system composed of a Docker-based backend, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The platform was tested in real-world deployment and assessed across server, mobile, and BLE-linked components. Our audit emulated realistic attack scenarios to evaluate the presence and severity of vulnerabilities affecting system confidentiality, integrity, and trust relationships [9, 10, 11].  
This study delivers both conceptual and practical contributions to the field of mHealth security. It combines theoretical threat modelling with experimental validation across layers, offering actionable insights for healthcare developers, system architects, and cyber-security professionals.  
The remainder of this paper is organized as follows: Section 2 reviews related work; Section 3 presents the platform architecture and attack surface; Section 4 details the results and provides a comparative analysis; Section 5 outlines the ethical considerations and experimental environment; Section 6 discusses risk classification and the experimental context; and Section 7 concludes this study.