A systematic literature review of cloud computing in eHealth

Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications

Integrated, reliable and cloud-based personal health record: a scoping review.

Personal Health Records (PHR) emerge as an alternative to integrate patient’s health information to give a global view of patients' status. However, integration is not a trivial feature when dealing with a variety electronic health systems from healthcare centers. Access to PHR sensitive information must comply with privacy policies defined by the patient. Architecture PHR design should be in accordance to these, and take advantage of nowadays technology. Cloud computing is a current technology that provides scalability, ubiquity, and elasticity features. This paper presents a scoping review related to PHR systems that achieve three characteristics: integrated, reliable and cloud-based. We found 101 articles that addressed thosecharacteristics. We identified four main research topics: proposal/developed systems, PHR recommendations for development, system integration and standards, and security and privacy. Integration is tackled with HL7 CDA standard. Information reliability is based in ABE security-privacy mechanism. Cloud-based technology access is achieved via SOA.CONACYT - Consejo Nacional de Ciencia y TecnologíaPROCIENCI

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

The Evolution of Access Control in Cloud Security: A Survey of Key Literature

Cloud Computing is a distributed model that enables users to access, meet and exchange resources and resource requirements hosted by various service providers. Authentication of requesting users and the permitting of their rights of access are extremely necessary in order to avoid unauthorised or unlawful access to cloud services. In the cloud scenario, access control of distributed resources is most critical. Access control is a basic aspect of the security of information that is directly linked to the fundamental features of confidentiality, honesty and availability. This article provides a literature analysis of the Cloud Computing Access Control Mechanism

Evaluation of Attribute-Based Access Control (ABAC) for EHR in Fog Computing Environment

Fog computing - a connection of billions of devices nearest to the network edge- was recently proposed to support latency-sensitive and real time applications. Electronic Medical Record (EMR) systems are latency-sensitive in nature therefore fog computing considered as appropriate choice for it. This paper proposes a fog environment for E-health system that contains highly confidential information of patients Electronic Health Records (EHR). The proposed E-health system has two main goals: (1) Manage and share EHRs between multiple fog nodes and the cloud,(2) Secure access into EHR on Fog computing without effecting the performance of fog nodes. This system will serve different users based on their attributes and thus providing Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog nodes and improve its performance. There are three major contributions in this paper first; a simulator of an E-health system is implemented using both iFogSim and our iFogSimEhealthSystem simulator. Second, the ABAC was applied at the fog to secure the access to patients EHR. Third, the performance of the proposed securing access in E-health system in fog computing was evaluated. The results showed that the performance of fog computing in the secure E-health system is higher than the performance of cloud computing

Rapid health data repository allocation using predictive machine learning

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020