IoT Health Data in Electronic Health Records (EHR): Security and Privacy Issues in Era of 6G

Abstract

Millions of wearable devices with embedded sensors (e.g., fitness trackers) are present in daily lives of its users, with the number growing continuously, especially with the approaching 6G communication technology. These devices are helping their users in monitoring daily activities and promoting positive health habits. Potential integration of such collected data into central medical system would lead to more personalized healthcare and an improved patient-physician experience. However, this process is met with several challenges, as medical data is of a highly sensitive nature. This paper focuses on the security and privacy issues for such a process. After providing a comprehensive list of security and privacy threats relevant to data collection and its handling within a Central Health Information system, the paper addresses the challenges of designing a secure system and offeres recommendations, solutions and guidelines for identified pre-6G and 6G security and privacy issues.

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

“…The possibility of security breaches or illegal access to highly confidential data has raised concerns [37]. To address these concerns, organizations must implement robust data security measures, such as encryption and access controls, to protect customer data from unauthorized access [38].…”

Real Time Patient Vital Monitoring and Alarm System with Prediction of Anomalies and Future Clinical Episodes using Machine Learning Models

“…However a few concerns regarding the security aspect of wearables and patient health data are a big concern as pointed out by Chacko and Hayajneh [3], Koren and Prasad [9]. They have highlighted the various threats to patient data considering their extremely sensitive nature such Medjacking where the data is tempered by a hacker.…”

Towards fostering the role of 5G networks in the field of digital health

“…For the first convolutional layer, an input image of 32 X32 X1 was used; the achieved accuracy of the proposed model was 99%. Comprehensive security analysis and privacy threat identification for IoMT medical data was presented in Koren and Prasad [16], where authors focused on general data protection regulation (GDPR). They highlighted possible IoMT security threats: weak IoMT cryptographic systems, bargained or rogue IoT devices, eavesdropping of communication channels.…”

“…Comparing different purposes such as core technology, application domain security architecture, blockchain and other perspectives. Security architecture for S-health [16], [30], [37] 3…”