Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda

Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth , that can be used for storing, accessing, and managing of e-healthcare data.

Blockchain for IoT-Based Healthcare: Background, Consensus, Platforms, and Use Cases

Healthcare data management has been gaining a lot of attention in recent years because of its high potential to provide more accurate and cost-efficient patient care. The traditional client-server and cloud-based healthcare data management systems suffer from the issues of single point of failure, data privacy, centralized data stewardship, and system vulnerability. The replication mechanism, and privacy and security features of blockchain have a promising future in the healthcare domain as they can solve some of the inherent issues of the health management system. However, most of the recent research works on blockchain in the healthcare domain have primarily focused on the permission-less Bitcoin network that suffers from drawbacks such as high energy consumption, limited scalability, and low transaction throughput. Consequently, there is a need for a scalable, fault-tolerant, secure, traceable and private blockchain to suit the requirements of the healthcare domain. We propose a lightweight blockchain architecture for the healthcare data management that reduces the computational and communication overhead compared to the Bitcoin network by dividing the network participants into clusters and maintaining one copy of the ledger per cluster. Our architecture introduces the use of canal, that allows secure and confidential transactions within a group of network participants. Furthermore, we propose a solution to avoid forking which is prevalent in the Bitcoin network. We demonstrate the effectiveness of our proposed architecture in providing security and privacy compared to the Bitcoin network by analyzing different threats and attacks. We also discuss how our proposed architecture addresses the identified threats. Our experimental results demonstrate that our proposed architecture generates 11 times lower network traffic compared to the Bitcoin network as the number of blocks increases. Our ledger update is 1.13 times faster. Our architecture shows a speedup of 67% in ledger update and 10 times lower network traffic when the number of nodes increases.

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Lightweight Blockchain for Healthcare

The Internet of Things (IoT) is a methodology or a system that encompasses real-world things to interact and communicate with each other with the assistance of networking technologies. This article describes surveys on advances in IoT-based healthcare methods and reviews the state-of-the-art technologies in detail. Moreover, this review classifies an existing IoT-based healthcare network and represents a summary of all perspective networks. IoT healthcare protocols are analyzed in this context and provide a broad discussion on it. It also initiates a comprehensive survey on IoT healthcare applications and services. Extensive insights into IoT healthcare security, its requirements, challenges, and privacy issues are visualized in IoT surrounding healthcare. In this review, we analyze security and privacy features consisting of data protection, network architecture, Quality of Services (QoS), app development, and continuous monitoring of healthcare that are facing difficulties in many IoT-based healthcare architectures. To mitigate the security problems, an IoT-based security architectural model has been proposed in this review. Furthermore, this review discloses the market opportunity that will enhance the IoT healthcare market development. To conduct the survey, we searched through established journal and conference databases using specific keywords to find scholarly works. We applied a filtering mechanism to collect only papers that were relevant to our research works. The selected papers were then examined carefully to understand their contributions/research focus. Eventually, the paper reviews were analyzed to identify any existing research gaps and untouched areas of research and to discover possible features for sustainable IoT healthcare development.

Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities

Blockchain, a form of distributed ledger technology has attracted the interests of stakeholders across several sectors including healthcare. Its’ potential in the multi-stakeholder operated sector like health has been responsible for several investments, studies, and implementations. Electronic Health Records (EHR) systems traditionally used for the exchange of health information amongst healthcare stakeholders have been criticised for centralising power, failures and attack-points with exchange data custodians. EHRs have struggled in the face of multi-stakeholder and system requirements while adhering to security, privacy, ethical and other regulatory constraints. Blockchain is promising amongst others to address the many EHR challenges, primarily trustless and secure exchange of health information amongst stakeholders. Many blockchain-in-healthcare frameworks have been proposed; some prototyped and/or implemented. This study leveraged the PRISMA framework to systematically search and evaluate the different models proposed; prototyped and/or implemented. The bibliometric and functional distribution of all 143 articles from this study were presented. This study evaluated 61 articles that discussed either prototypes or pilot or implementations. The technical and architectural analysis of these 61 articles for privacy, security, cost, and performance were detailed. Blockchain was found to solve the trust, security and privacy constraints of traditional EHRs often at significant performance, storage and cost trade-offs.

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A Systematic Review of Blockchain in Healthcare: Frameworks, Prototypes, and Implementations

Multi-tier Blockchain Framework for IoT-EHRs Systems

The rapid development of tools for communication such as social networks, tweeting and Whatsapp has generated a large mass of important textual data. Also, the COVID-19 pandemic has inflamed social networks, hence the automatic analysis of opinions has become paramount. The purpose of this paper is to analyze Arabic tweets in terms of positivity, negativity, or neutrality.In analyzing the opinions of the Arabic language, a real challenge is encountered, which lies in the use of different dialects (Egyptian, Saudian, Maghrebian, Gulfian, Levantine, Syrian $\ldots $ ). In this paper, we introduce two major components: The first employs six machine learning (ML) methods, including Decision Trees (DT), Logistic Regression (LR), k Nearest Neighbors (K-NN), Random Forests (RF), Support Vector Machines (SVM), and Nave Bayes (NB), with the TF-IDF method acting as the feature extraction.While, the second part consists of testing three variants of Deep Learning (DL) based on multiplicative Long Short Term Memory (mLSTM), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) by applying word embedding as the input vector. The experimental study was validated using three Arabic language corpora (TEAD, ATSAD, and ASTD) and two learning modes (Hold out and 10-folds cross validation). The obtained results in terms of Accuracy (ACC), Precesion (PREC), Recall (REC), and F1-score (F1) show a clear performance for DL techniques based on a 10-folds strategy compared to the state-of-the-art. The experiments shown in the paper reveal that the proposed DL models accomplished the best results.

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ArabicDialects: An Efficient Framework for Arabic Dialects Opinion Mining on Twitter Using Optimized Deep Neural Networks

Since the era’s of Cloud computing beginning, the Identity Management is considered as a permanent challenge especially for the hybrid IT environments that permit for many users’ applications to share the same data center depending on servers’ virtualization. This paper introduces a complete study
about Identity forms in different domains and applications. Also, a performance evaluation of new approaches for Virtual Identity was done. Actually, Virtual Identity, a new terminology used in virtual environments, was introduced to enhance the anonymous communication in such types of complex networks. Based on the work analysis and motivations done through an online survey, two techniques were used to implement the Virtual Identity; Identity Based Encryption (IBE) and Pseudonym Based Encryption (PBE). Both techniques were validated using MIRACL library for security algorithms. In addition, the performance of both approaches was evaluated under different configurations and network conditions through OPNET Modeler. The results showed the impact of the number of cloud users and their locations (either local or remote) on the application response time in cloud environments using the proposed virtual identities. Moreover, Application Characterization Environment whiteboard was used to simulate the overall flow of data across different tiers from start to end of the application task for Virtual Identity creation. The results and outcomes for both methodologies showed that they are suitable paradigm for achieving high degree of security and efficiency in such sophisticated network access to many online services and applications.

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Virtual Identity Approaches Evaluation for Anonymous Communication in Cloud Environments

Recently, the automatic process of Arabic opinion mining (AOM) attracts the intention of scientific researchers, especially with the real challenges of the arabic language as dialectal language, Arabizi format, which combine multilingual forms. The prepossessing aims to realize a filtering operation on text, then TF-IDF is applied in order to transform the text to numerical vector. The main drawback of TF-IDF arises on the dimensionality. Thus, the feature selection plays a vital role for selecting the relevant words using swarm intelligence algorithms. The primary objective of this paper is to use the Marine Predators Algorithm (MPA) to determine the nature of opinions in the Arabic language in order to select the most pertinent terms. The experimental study is conducted on OCA data-set, and the results indicate that the proposed algorithm MPA outperforms other algorithms inspired by swarm intelligence and others from the literature in terms of accuracy and selection ratio.

Ibrahim A. Gomaa

Papers

Multi-tier Blockchain Framework for IoT-EHRs Systems

ArabicDialects: An Efficient Framework for Arabic Dialects Opinion Mining on Twitter Using Optimized Deep Neural Networks

Virtual Identity Approaches Evaluation for Anonymous Communication in Cloud Environments

AOM-MPA: Arabic Opinion Mining using Marine Predators Algorithm based Feature Selection

A Novel Virtual Identity Implementation for Anonymous Communication in Cloud Environments