Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

Abstract

In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system’s layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system’s limitations.

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

“…RL emerges as an effective method for malware detection in IoMT, leveraging feedback mechanisms to enhance decision-making. Studies by Alavizadeh et al [14] and Rafik et al [15] demonstrate RL's efficacy, with deep Q-learning algorithms detecting intrusions with high accuracy. RL's adaptability to evolving threats and its real-time operation highlight its potential in securing IoMT devices, despite challenges such as the need for substantial training data and algorithm complexity.…”

Data Security Design in Computer Application Software Development from the Perspective of Information Security Environment

“…This must be designed from an overall system security perspective and ensure that security requirements are fully considered when designing and implementing the system. Software design and architecture based on security best practices implement appropriate authorization and access control mechanisms, as well as effective authentication and authorization mechanisms [9][10]. Sensitive data must be encrypted, stored, transmitted and processed using appropriate encryption algorithms and protocols to ensure that data is not easily stolen, altered or disclosed during storage and transmission.…”

Securing Medical Data Exchange: A Decentralized Approach Based on the e-IPGPChain Framework

“…Edge computing is considered a closer resource gateway to the end user [1], which offers low latency, storage, and computing resources. This technology is exploited to enable the cryptographic tool GPG for conducting data processing operations, based on various supported cryptographic mechanisms and algorithms as listed in the Table 2.…”

“…Exchange Data over multiple entities has increased recently in different sectors, due to the high digitalization expansion over various industries including Education, Financial Services, Commerce, and Healthcare sector. Health services have increased recently due to the expansion of viral and chronic diseases which raise medical requirements [1]. However, the Traditional e-health smart systems are revolutionizing the healthcare industry by providing medical services closer to end users based on multiple typical layers such as edge computing, Internet of Medical Things "IoMT" and Cloud computing technologies [1], which aim to manage medical records that are commonly known as Electronic Health Records "EHR".…”

“…Health services have increased recently due to the expansion of viral and chronic diseases which raise medical requirements [1]. However, the Traditional e-health smart systems are revolutionizing the healthcare industry by providing medical services closer to end users based on multiple typical layers such as edge computing, Internet of Medical Things "IoMT" and Cloud computing technologies [1], which aim to manage medical records that are commonly known as Electronic Health Records "EHR". The exponential increase of data over time puts an overload to the cloud networks for processing and storing data as well as difficult accessibility for various stakeholders, this results in serious performance, security, and privacy challenges due to its limited centralized built-in structure.…”