Journal:A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions

From LIMSWiki
Jump to navigationJump to search
The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.
Full article title A scoping review of integrated blockchain-cloud architecture for healthcare: Applications, challenges, and solutions
Journal Sensors
Author(s) Ismail, Leila; Meterwala, Huned; Hennebelle, Alain
Author affiliation(s) United Arab Emirates University
Primary contact Email: leila at uaeu dot ac dot ae
Editors Yu, Keping
Year published 2021
Volume and issue 21(11)
Article # 3753
DOI 10.3390/s21113753
ISSN 1424-8220
Distribution license Creative Commons Attribution 4.0 International
Website https://www.mdpi.com/1424-8220/21/11/3753/htm
Download https://www.mdpi.com/1424-8220/21/11/3753/pdf (PDF)

Abstract

Blockchain is a disruptive technology for shaping the next era of healthcare systems striving for efficient and effective patient care. This is thanks to its peer-to-peer, secure, and transparent characteristics. On the other hand, cloud computing made its way into the healthcare system thanks to its elasticity and cost-effective nature. However, cloud-based systems fail to provide a secured and private patient-centric cohesive view to multiple healthcare stakeholders. In this situation, blockchain provides solutions to address security and privacy concerns of the cloud because of its decentralization feature combined with data security and privacy, while cloud provides solutions to the blockchain scalability and efficiency challenges. Therefore a novel paradigm of blockchain-cloud integration (BcC) emerges for the domain of healthcare.

In this paper, we provide an in-depth analysis of the BcC integration for the healthcare system to give the readers the motivations behind the emergence of this new paradigm, while also introducing a classification of existing architectures and their applications for better healthcare. We then review the development platforms and services and highlight the research challenges for the integrated BcC architecture, possible solutions, and future research directions. The results of this paper will be useful for the healthcare industry to design and develop a data management system for better patient care.

Keywords: blockchain, cloud computing, electronic health records, health data analytics, healthcare system, security, privacy

Introduction

The healthcare domain has been revolutionized over the last century by technological advancement.[1] This revolution aims to improve the diagnosis of diseases and their causes, the quality of medical supplies, and the quality of medical treatment, as well as establish prevention plans on a global scale. The traditional client-server-based healthcare systems[2][3][4][5][6] suffer from security and privacy issues and lead to scattered patient’s medical history, delaying patient treatment.[7][8] Moreover, a patient needs to repeat medical tests when moving to another hospital. This increases the cost and time to the patient and affects the patient’s health due to repeated exposure to tests, such as X-rays and MRIs, each with their own potential side effects.[9] In addition, healthcare organizations are required to install and maintain infrastructure with up-to-date functionalities while complying with healthcare standards and regulations for the management of electronic health records (EHRs). This leads to a high total cost of ownership. To address these limitations of the client-server-based approach, the on-premises database migrated to the cloud, where health records are maintained by a cloud service provider.

Cloud computing[10] allows convenient and on-demand network access to a shared pool of configurable computing resources. Motivated by the pay-as-use cloud model, medical organizations use cloud computing to manage EHRs, reducing the cost of ownership. The five-year cost of $11 million for an on-premises healthcare system can be reduced to $3.2 million using cloud. This also reduces the infrastructure set-up time from 16 weeks to one week (Healthcare system cost reduction using cloud-based approach.[11] In addition, cloud computing provides multiple healthcare providers with efficient access to health records from a shared storage, improving patient care. The number of health records is increasing at a rapid pace with the introduction of smart healthcare and internet of things (IoT) devices with biosensors for personalized patient-centric healthcare. The scalability and elasticity of cloud computing aid in health records management, which requires powerful computing and large storage, for near real-time patient care. However, a cloud-based system suffers from the issues of security, i.e., data integrity, where the health records are under a constant threat of being modified, and privacy, i.e., unobservability or data leakage, in which the patients’ health records are being used without any tracking.[12]

Recent years have witnessed a bit of a revolution, with blockchain being adopted for many applications in the healthcare domain, such as health records management[13][14][15][16][17], medical supply chain management[18][19], and medical insurance claim management.[20][21] The characteristics of blockchain give it great potential for providing a patient-centric healthcare system, involving health stakeholders such as patients, health professionals, insurance providers, pharmaceutical firms, and health governmental authorities.

From a technical aspect, blockchain is a peer-to-peer distributed system, which enables users to maintain a ledger of transactions that is replicated over multiple servers.[22] The architecture allows all the network participants, i.e., healthcare stakeholders, to verify and process health data transactions without the need for a trusted third party. In addition, the data stored in the blockchain is immutable, i.e., once the data is stored, it cannot be modified or deleted, leading to enhanced security. This immutability enables an audit trail, bringing in accountability, adding trust to the system, and alleviating privacy concerns.[23][24] These distinctive features of blockchain have triggered its wide adoption for health records management to address information security and privacy issues, while providing access to patient’s health history to multiple stakeholders for patient-centric health services. However, blockchain poses scalability issues as the network grows[25], and consequently more hardware and human resources have to be provisioned for the operation and maintenance of the blockchain platform, thus increasing the health organization’s on-site cost. Moreover, blockchain suffers from the issues of high energy consumption[26][27], adding to blockchain operational cost.

Several works in the literature study the strengths and weaknesses of stand-alone cloud- or blockchain-based healthcare systems. To tackle security and privacy issues prevailing in cloud healthcare systems, the scalability issues inherent from the blockchain algorithms, and to develop more robust solutions for efficient patient care, research efforts have proposed applications using an integrated blockchain-cloud (BcC) paradigm, as shown in Figure 1. However, to the best of our knowledge, there is no work that analyzes the underlying BcC architectures or gives insights and research directions on how they can be enhanced for a patient-centric healthcare system.


Fig1 Ismail Sensors21 21-11.png

Figure 1. Evolution of the healthcare system toward an integrated blockchain-cloud (BcC) paradigm

The main contributions of this paper are as follows:

  • We present the limitations of a healthcare system that is based on either cloud or blockchain and highlight the importance of implementing an integrated BcC system for better patient care.
  • We present a scoping review and devise a taxonomy of existing integrated BcC healthcare system architectures into two different types based on the nature of integration. We analyze the effectiveness and limitation of these architectures.
  • We compare and analyze blockchain as a service (BaaS) platforms provided by different cloud service providers.
  • We identify the research challenges prevailing in an integrated BcC healthcare system and possible solutions that are proposed for these issues.

The rest of the paper is organized as follows. The next section presents an overview of related work. Afterwards, the concept of cloud computing, blockchain, and the importance of an integrated BcC healthcare system is discussed. The fourth section present a taxonomy, strengths, and weaknesses of the integrated BcC architectures, followed by an examination of different healthcare applications using the integrated BcC architecture, and then a discussion of the research challenges in BcC system, and possible solutions. Discussion and conclusions are presented in the final two sections.

Related work

Traditional healthcare systems are based on the client/server approach, where the patients’ health records are stored in a hospital’s centralized database. Later on, the healthcare system migrated to the cloud-based approach, where the patients’ health records are stored and managed in third-party cloud storage by a cloud service provider. This change was intended to solve the issues of scalability, high cost, fragmented patient records, and repeated medical testing problems associated with the client/server-based approach. Several works in the literature present a review on this type of cloud computing-based healthcare.[28][29][30][31] Hu and Bai[28] in their review classify the work on cloud-based healthcare into three categories depending on the area of focus: a framework for data sharing, healthcare applications, and security and privacy. The authors highlight the issues of data integrity and confidentiality and suggest that a hybrid cloud model with appropriate access control can be a reliable solution. Ali et al.[29] review applications of the cloud-based healthcare system and highlight the issues of security and privacy with the involvement of a centralized third party, i.e., the cloud service provider. Mehraeen et al.[30] in their review on security challenges in cloud-based healthcare systems advise having authentication, authorization, and access control to ensure data security. The authors found that identity management, internet-based access, cybercriminals, authorization, and authentication are the major concerns in the cloud-based healthcare system. Ermakova et al.[31] classify the literature on cloud-based healthcare into four contribution categories: framework development, application development, broker development, and security and privacy mechanisms development. However, they found the broad cloud-based approach suffers from the issues of centralization, data security, and privacy.

Blockchain, a peer-to-peer network, solves the issues existing in the cloud-based approach. This is because of the immutability, replication, and decentralization characteristics of the blockchain. Several authors have contributed to the literature concerning the blockchain-based healthcare system.[32][33][34][35][36] Hölbl et al.[32] analyze the literature based on the contributions of blockchain, i.e., framework/architecture, algorithms, consensus mechanisms, bench-marking metrics, and applicability. The application domains are classified into data sharing, access control, audit trail, and supply chain. The authors highlight the significant use of the technology for data sharing and access control compared to that for supply chain management and audit trail. Kuo et al.[33] present a systematic review of different blockchain platforms with healthcare examples, highlighting the platforms’ features such as network type, consensus protocol used, hardware requirements, smart contract support, transaction throughput, scripting languages, and open source support. Agbo et al.[34] categorize the work in the literature based on their areas of contributions such as electronic medical records (EMR) sharing, supply chain management, biomedical research and education, remote patient monitoring, health insurance claims, and health data analytics. The authors reveal that the application of blockchain technology in the area of healthcare is limited due to the issues of interoperability, scalability, execution time, and patient engagement. Vazirani et al.[35] assess the feasibility of blockchain for efficient EHR management and conclude that the use of blockchain for healthcare can solve the issues of interoperability, security, and privacy. Hussien et al.[36] categorize the work on blockchain-based healthcare according to their applicability such as clinical/medical data sharing, remote patient monitoring, clinical trials, and health insurance.

Table 1 shows the existing reviews on either cloud-based or blockchain-based healthcare systems. However, to the best of our knowledge, no work analyzes the concept of an integrated BcC architecture for healthcare. In this paper, we highlight the motivation for the integrated BcC architecture for better patient-centric healthcare. In addition, we discuss the evolution of these architectures and capture the assumptions that led to their development. We provide a classification of the integrated architectures and their applications for better healthcare. We then review the BcC developments platforms and services and highlight the research challenges, possible solutions, and future research directions.

Table 1. Related reviews
Work Healthcare system approach Area of focus Contribution(s)
Hu and Bai[28] Cloud computing Cloud computing in e-health Categorization of cloud-based works, organized into four studied areas: (1) framework, (2) application, and (3) security and privacy.
Ali et al.[29] Cloud computing Opportunities, challenges and applications of cloud-based healthcare Analysis of cloud computing-based healthcare in terms of opportunities (management and technical), issues (technical, legal, security, and privacy), and applications (information processing and monitoring). Discussion of research and implementation implication of the system.
Mehraeen et al.[30] Cloud computing Security challenges in cloud-based healthcare Investigation of security challenges and recommendations for secure communication and interoperability in cloud-based healthcare.
Ermakova et al.[31] Cloud computing Cloud computing in healthcare Categorization of cloud-based works based on four contribution areas: (1) framework development, (2) application development, (3) broker development, and (4) security and privacy mechanisms development.
Hölbl et al.[32] Blockchain Blockchain in healthcare Categorization of blockchain-based works based on contributions (framework/architecture, algorithm, consensus protocol, and bench-marking metric) and applicability (data sharing, access control, audit trail, and supply chain management).
Kuo et al.[33] Blockchain Blockchain platforms, with healthcare as an example. Comparison of blockchain platforms based on the following features: (1) network type, (2) consensus protocol used, (3) hardware requirement, (4) smart contract support, (5) transaction throughput, (6) scripting language, and (7) open source support.
Agbo et al.[34] Blockchain Blockchain in healthcare Overview of blockchain and categorization of blockchain-based work based on six contribution categories: (1) EMR sharing, (2) supply chain management, (3) biomedical research and education, (4) remote patient monitoring, (5) health insurance claim management, and (6) health data analytics. Challenges and limitations of a blockchain-based healthcare system
Vazirani et al.[35] Blockchain Blockchain implementation in healthcare Assessment of blockchain feasibility for efficient EHR management.
Hussien et al.[36] Blockchain Taxonomy, challenges, and recommendations for a blockchain-based healthcare system Overview of blockchain and categorization of blockchain-based work based on four types of applicability: (1) clinical/medical data sharing, (2) remote patient monitoring, (3) clinical trials, and (4) health insurance. Motivation, challenges, and recommendation for a blockchain-based healthcare system.
This work Integrate blockchain-cloud (BcC) Taxonomy of integrated BcC healthcare system architectures, challenges, and solutions Importance of an integrated BcC healthcare system and taxonomy of existing BcC architectures. Comparison of integrated BcC platforms. Survey of different healthcare application domains benefited by integrated BcC. Discussion of issues existing in an integrated BcC healthcare system, along with possible solutions for future research directions.

Background and motivation

In this section, we first explain the fundamental concepts of cloud and blockchain technologies to aid in a better understanding of the rest of the paper. Then, we highlight the motivation of an integrated BcC healthcare system.

Background

Cloud computing

Cloud computing technology offers a shared pool of configurable hardware resources and software services over the internet.[10] These resources can be speedily allocated and released without the system administrator’s intervention. Cloud computing is mainly characterized by on-demand service, rapid elasticity, pay-per-use model, and multi-tenancy. Figure 2 shows the general overview of the cloud system architecture.


Fig2 Ismail Sensors21 21-11.png

Figure 2. Overview of a cloud system architecture


Broadly speaking, a cloud architecture consists of

  1. cloud consumers who are individual users (patients and allied healthcare professionals) and/or organizations (hospitals) that use the cloud services;
  2. a cloud broker that enables the communication between the cloud consumers and the cloud; and
  3. a cloud entity that makes the cloud services available to the consumers.

This architecture can then be organized into three layers: a physical resource layer, a resource abstraction and control layer, and a service layer. The physical layer consists of the hardware resources for processing, storage, and networking, and the facility resources for cooling, ventilation, power, and supply. The resource abstraction and control layer consists of the system components that enable access to the physical resources through a software abstraction. Abstraction components include virtual computing and virtual storage elements. This layer is also responsible for the efficient allocation and usage monitoring of the physical resources. The service layer consists of the interfaces required to access the cloud services. These cloud services can then be classified into software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). SaaS makes software available remotely to multi-tenant users as a web-based service, e.g., Google Mail. PaaS provides the environment and tools required to develop web-based applications, e.g., Amazon Web Services (AWS). IaaS offers virtualized hardware hosted in cloud data centers to the end-users for operations. The hardware involves storage, computing servers, and network components. NTT Communications is an example of IaaS.

The cloud network can be divided into multiple categories. The three main categories are:

  • Public cloud: Allows public access to systems and services without any restrictions and is less secure.
  • Private cloud: Allows members of the organization that manages the cloud to access the systems and services and is more secure than a public cloud. A private cloud when shared among multiple organizations is known as a community cloud.
  • Hybrid cloud: Combination of a public and private cloud that enables greater flexibility. The critical and confidential activities can be managed using the private cloud, while the general activities can be managed using the public cloud.

With the emergence of cloud computing, the healthcare system migrated from client-server-based infrastructure to cloud-based infrastructure. Cloud solves the issues of fragmented health records and the high total cost of ownership existing in the client-server-based healthcare system. This is thanks to the on-demand access, replication, and pay-as-you-go characteristics of the cloud. A cloud-based healthcare system is implemented using a private cloud to allow only authorized data access based on access control rights. Several cloud-based healthcare systems are proposed in the literature, where a patient or allied health professional can obtain a cohesive view of the patient’s medical history stored in third-party cloud storage.[37][38][39] Although the cloud-based approach improves system scalability and reduces the total cost of ownership, the health records managed by the cloud service provider are under constant security and privacy threats.[40][41] The patients’ records can be tampered with or can be accessed without their knowledge.[11] Consequently, a more robust healthcare management system is required to address the shortcomings of the cloud-based approach.

Blockchain

Blockchain is a peer-to-peer distributed system that maintains a synchronized ledger of transactions that is replicated over network participants. It was introduced for the exchange of e-currency in a network without the intervention of a third party.[42] Since then, blockchain has spread in several application domains such as healthcare, education, industry and marketplace, digital media, government, and entertainment. Blockchain has the following properties:

  • Decentralization: Blockchain eliminates the intervention of a third-party entity for the processing of transactions and maintaining the ledger data. The transactions are validated and executed by the agreement of the majority of the participants that maintain the network.
  • Immutability: The blockchain is a continuous chain of blocks where a block is connected to its preceding block by including the hash of the latter while hashing the former. A block is composed of a block header consisting of metadata and a block body consisting of valid transactions.[22] If a malicious entity attempts to tamper with the data of a block in the past, the hash of the block will change leading to a different hash value than the one used to calculate the hash of the succeeding block. Consequently, the malicious entity needs to re-hash all the subsequent blocks in the chain up till the last block. This re-hashing is compute-intensive, especially when there are several replicated copies of the ledger in the network. Thus, any data modification attempt is discouraged, leading to immutability.
  • Transparency: Each operation performed in the network to access the data stored in the ledger is considered as a transaction in the blockchain. Each node in the network that holds the copy of the ledger can track any unauthorized or malicious data access, making the blockchain secure and transparent.
  • Traceability: The replicated ledger in the blockchain enables efficient tracing of any transaction by the nodes maintaining the ledger. This discourages any malevolent activity, making the network more secure, efficient, and transparent.
  • Consensus: Each transaction in the blockchain is verified and processed by the agreement of most of the participants holding the ledger copy. This enables transactions between participants who do not know and trust each other.

Figure 3 shows how a transaction is processed in the blockchain network. To initiate a new transaction, the transaction data is hashed by the transaction initiator, such as allied health professionals and patients. The digital signature of the transaction is generated by encrypting the hashed data. The encryption is performed using the private key of the transaction initiator. The transaction data and the corresponding digital signature are broadcasted to the network for processing. Each validating node in the network validates the transaction when received. This is by ensuring the authenticity of the transaction initiator and the integrity of the transaction data. The authenticity is verified if the digital signature is successfully decrypted using the transaction initiator’s public key. The integrity is verified if the hashed data obtained from the decryption operation matches the hash of the transaction data. The transaction, if valid, is broadcasted in the network to include it in the block. A miner (node that generates a block) creates a block of the received valid transactions after verifying each transaction for its validity. The selection of a miner that generates a block and the procedure of verifying and appending the generated block to the chain depends on the consensus protocol used by the blockchain network. The consensus protocols in blockchain are classified into compute-intensive-based, capability-based, and voting-based.[22] The selected miner generates the hash of the block, also known as the digital signature, and broadcasts the block in the network. The block’s hash is generated by first hashing the block header and then hashing the obtained hashed value. The version in the block header represents the version of the protocol used and the timestamp represents the block generation time. The Merkle root is a single hash value obtained from iterative pair-wise hashing of the transactions in the block data. Each validating node will update their ledger copy by adding the block if valid.[22]


Fig3 Ismail Sensors21 21-11.png

Figure 3. Processing of a transaction in blockchain

The blockchain network can be a public, private, consortium, or hybrid. The public network is the one where any entity can join the network with no prior permission and view the transaction data. On the other hand, a private network, organized by a single organization, is one where participation is subjected to prior permission and the data can be accessed based on access control rights. A private blockchain is suitable for healthcare as only authorized members can join the network and the ledger is queried and updated using access control rights. A consortium blockchain is such that a group of predetermined organizations governs the network. A hybrid blockchain lies somewhere between the public and the private versions, where the ledger can be viewed by any network participant, but the modifications to the ledger are subject to access control. The distinctive features of the blockchain described above are promising for the healthcare domain. A blockchain-based healthcare system has the following benefits:

  • Provenance: The immutable blockchain ledger enables an audit trail, increasing the trust in the network. Any fraud in the network along with its source can be easily traced. This discourages malicious activities.
  • Protection against natural disasters: In case of natural disasters such as forest fires, hurricanes, and floods, a database and its regional replicas might be unavailable. In such a scenario, the globally replicated blockchain ledger can aid in fault tolerance.
  • Real-time data access: Patient’s health records can be accessed in real-time from the local or the nearest copy of the ledger to avoid life-threatening situations.
  • Accurate patient care: The cohesive view of a patient’s health records provided by the blockchain enables allied health professionals in making a better prognosis/diagnosis.

Several blockchain-based healthcare data management systems have been proposed in the literature.[13][14][15][16][17] However, with the increasing amount of health records, the scalability[25][43] and energy consumption[26][27] of blockchain is an issue. In addition, on-premises blockchain deployment increases the total cost of ownership for healthcare organizations.

Motivation of integrated BcC for healthcare

Security and privacy are the main requirements for an effective, trustworthy, patient-centric, and accurate healthcare system. The cloud-based system provides scalability and cost-effectiveness for managing ever-growing health records. However, security and privacy threats become a critical issue due to the involvement of a third-party service provider. Consequently, the healthcare domain seeks a more robust solution for the management of health records. Blockchain, a peer-to-peer network, allows transactions between multiple network participants, eliminating the need for a third party. Every event in the network is recorded on an immutable ledger, which is replicated over multiple network nodes. Blockchain enables transparent auditing, authorized data access, and immutability, thus providing secure and private management of health records. However, the scalability and the total cost of ownership question the implementation of blockchain in the healthcare domain where the number of health records is continuously increasing. As such, integrating cloud with blockchain (BcC) in healthcare enhances scalability and reduces cost while maintaining the security and privacy of health records.

Recently, there has been growing interest in AI-based healthcare, where the health records are analyzed using AI and machine learning algorithms to support allied health professionals with better prognosis and diagnosis of diseases. The accuracy of the AI and machine learning can be improved, resulting in a more accurate diagnosis and prognosis of a disease when more instances of data are used for training the models. In this context, an integrated BcC healthcare system would certainly revolutionize the way health professionals provide patient care. Blockchain would facilitate private and secure integration of data from multiple hospitals leading to a rich, secure and accurate database for the AI models, and cloud computing would enhance the scalability of the system. The incorporation of AI within an integrated BcC healthcare system could lead towards a better patient-centric, secure, and private healthcare, where the high availability of data from multiple sources, thanks to blockchain, can aid in better diagnosis and prognosis of disease using AI and machine learning techniques in a scalable cloud environment.

Taxonomy and the strengths and weaknesses of integrated BcC healthcare system architectures

The individual benefits of cloud and blockchain technologies have led to the emergence of integrated BcC architectures, where the limitations of the standalone approaches are addressed. In this section, we present an analysis and classification of those architectures, and we compare the BcC development platforms and services.

Encapsulated architecture

In this architecture, the blockchain platform and its underlying implementation are encapsulated within a cloud environment, as shown in Figure 4.


Fig4 Ismail Sensors21 21-11.png

Figure 4. Encapsulated BcC architecture for healthcare.

We formulate encapsulated architecture as:

This architecture has been proposed by several works in the literature.[44][45][46][47][48][49][50] The network participants (users) are the different health stakeholders, including allied health professionals, patients, health insurance companies, pharmaceutical firms, and health governmental authorities. The allied health professionals include doctors, nurses, dietitians, medical technologists, therapists, and pathologists. The users can connect to the platform via remote procedure call (RPC), representational state transfer (REST) application programming interface (API), web API, or SOAP (Simple Object Access Protocol). Health records can be generated by the allied health professional upon a patient’s visit or by the patient using sensors. A gateway device is used to process the sensor data. The cloud platform consists of a certificate authority, security management module, and operation management module, in addition to the blockchain (as a service). The security management module involves identity and access management, cloud firewall, and web application firewall, and the operation management module includes bill management, data replication and recovery, resource monitoring (CPU, memory, and storage usage), and a log service. The blockchain encapsulated within the cloud consists of an application layer, distributed computing layer, and storage layer. The blockchain ledger in the cloud database is stored using the InterPlanetary File System (IPFS)[51] or Storj Decentralized Cloud Storage.

The health transaction execution flow in this architecture is as follows:

Step 1: A transaction initiator (network participant) hashes the health record (transaction payload).
Step 2: The digital signature of the payload is generated by encrypting the hashed transaction.
Step 3: The transaction payload along with the digital signature is broadcasted to the blockchain nodes running in the cloud instances.
Step 4: The transaction is validated, and the block is generated based on the consensus mechanism.
Step 5: The block is updated to the ledger.

Several cloud service providers such as Microsoft Azure[52], Amazon Web Services[53], and Oracle[54] offer cloud-based solutions to help organizations adopt blockchain with ease. In 2015, Microsoft introduced Ethereum Blockchain as a Service (EBaaS) on its cloud platform Azure.[55] With BaaS, the compute- and storage-intensive blockchain runs in the cloud and is managed by the cloud service provider. Blockchain is offered as a service, like any other cloud service, to the consumers (healthcare organizations) to develop and host their blockchain solutions, functions, and smart contracts. The organizations are only charged based on what they use, thanks to the pay-as-you-go cloud model. For instance, BaaS offered by Amazon Web Services charges users $0.067/hour for a medium instance peer node, $0.10/GB-month for node storage and data written to the network, and $0.05/GB for more than 150 TB/month data transfer.[56]

Table 2 shows the encapsulated architecture-based cloud platforms that offer BaaS. It shows the blockchain development platforms supported by these cloud platforms, the type of blockchain network, and the consensus mechanism used. In addition, it states whether or not the platform supports channel communication. A channel is a private sub-network of communication between specific network participants to perform private and confidential transactions.[57] The channel has its ledger which can only be accessed by the channel members. This is in addition to the main blockchain ledger. The concept of "channel" is important for healthcare applications in situations such as confidential patient treatment, biomedical research, and formulation of government policies and prevention plans.

Table 2. Encapsulated architecture-based development platforms
Encapsulated BcC platforms Blockchain network Consensus Description Channel support
Cloud Blockchain
Microsoft Azure[52] Ethereum, Hyperledger Fabric, Corda, Chain, and Quorum Consortium Istanbul byzantine fault tolerance Azure Blockchain Service is a BaaS with built-in consortium management that enables quick network deployment and operations with smart contract capabilities. It can be deployed using Azure portal/CLI or through Microsoft Visual Studio Code using the Azure blockchain extension. The services are offered in two tiers: (1) basic, for development and testing; and (2) standard, for deployment. Yes (Hyperledger Fabric)
Amazon[53] Hyperledger Fabric Consortium - Amazon Managed Blockchain enables easy creation of blockchain networks. The platform uses a voting API, that allows network participants to vote for adding/removing members. Yes
Oracle[54] Hyperledger Fabric Hybrid Raft Oracle Blockchain Platform enables blockchain configuration, development, and execution of smart contracts, and monitoring through a web console. External applications update/query via client software development kits (SDKs) or REST API calls. Yes
IBM[58] Hyperledger Fabric Private, public, and hybrid Pluggable consensus IBM Blockchain Platform allows users to develop, test, and deploy blockchain applications with smart contract capabilities using the Visual Studio code extension. The platform supports multiple languages for the development of smart contracts Yes
Google[59] Ethereum Hybrid Pluggable consensus Google blockchain enables deployment of blockchain applications with easy API integration. It allows the use of a traditional SQL database for blockchain data update/query. No
SAP[60][dead link] Multichain, Hyperledger Fabric, and Quorum - - SAP Cloud Platform Blockchain Service enables development and deployment of blockchain applications from scratch, allows linking to external blockchain nodes to the cloud, or allows connecting an external blockchain to SAP’s powerful memory data platform, HANA. Yes (Hyperledger Fabric)
Hewlett Packard Enterprise[61] Ethereum - - HPE Mission Critical Blockchain enables fault tolerant and highly scalable blockchain application development with smart contract integration. No
Alibaba[62] Hyperledger Fabric, Ant, and Quorum Consortium - Alibaba Cloud BaaS is developed on top of the Alibaba cloud container service for Kubernetes clusters, enabling quick development and deployment of blockchain solutions. Alibaba Cloud BaaS API allows users to manage blockchain objects and cloud resources. Yes (Hyperledger Fabric)
Huawei[63] Hyperledger Fabric Consortium Solo, fast byzantine fault tolerance, and Kafka Huawei Blockchain Service, based on Huawei containers, enables easy creation, deployment, and management of blockchain solutions. Yes (Hyperledger Fabric)
Baidu[64] Permissioned Ethereum, Hyperledger Fabric, and Baidu XuperChain - Pluggable consensus Baidu BaaS enables easy development and deployment of blockchain applications, with multichain and smart contracts features. Yes

In summary, encapsulated BcC healthcare system architecture incorporates blockchain technology and its functionalities within the cloud platform. The healthcare stakeholders have to trust the cloud service provider as the underlying blockchain is implemented and managed by the latter. Consequently, security and privacy issues are not completely addressed by the encapsulated BcC architecture. In this architecture, the system is upgraded by the cloud service provider.

Non-encapsulated architecture

To address the issues of security and privacy existing in encapsulated BcC architecture, non-encapsulated BcC architecture has been proposed in the literature.[65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] Broadly speaking, non-encapsulated BcC means the cloud and blockchain technologies are integrated without encapsulating one into another, as shown in Figure 5.


Fig5 Ismail Sensors21 21-11.png

Figure 5. Non-encapsulated BcC architecture for healthcare

We formulate non-encapsulated architecture as:

Compared to an encapsulated architecture, where the blockchain ledger consisting of health records is managed by the cloud service provider, in non-encapsulated architecture the health records are managed in the cloud database while the associated metadata (e.g., a health record’s hash, record update, and query events) and access control policy is recorded in the blockchain. The medical records in the cloud database are stored using IPFS or Storj. The blockchain ledger is replicated across multiple healthcare organizations’ databases. Compared to encapsulated architecture, non-encapsulated architecture consists of an additional integrator component. The integrator enables communication between the cloud and blockchain platforms. The health transaction execution flow in this architecture is executed as follows:

Step 1: The health record data is encrypted by the transaction initiator (network participant) and broadcasted to the third-party cloud database.
Step 2: The data is stored in the cloud database.
Step 3: The metadata of the health record (e.g., the hash of the data, the address in the cloud where the data is stored, and the access control list containing the IDs of the authorized participants) is sent to the blockchain by the integrator.
Step 4: The metadata is recorded in the blockchain as a transaction, and the ledge is updated upon consensus.

The off-chain storage for health records in the cloud database enhances the scalability of the system, whereas the metadata of the transactions in the blockchain ledger aids in security and privacy. The inclusion of the health record’s hash in the blockchain transaction ensures the integrity of the record, and the inclusion of record update and query events discourages unobserved access, enhancing system privacy. Table 3 shows the contents of the off-chain storage and the blockchain transactions for the non-encapsulated architecture proposed in the literature.

Table 3. Contents of off-chain cloud storage and blockchain transaction in non-encapsulated BcC architectures proposed in the literature
Work Cloud database Blockchain transaction
Transaction types Inclusion of health record's hash Access control policy
Record update event Record query event
Du et al.[65] Encrypted health record
Thwin and Vasupongayya[66]
Zheng et al.[67]
Rouhani et al.[68]
Wang and Song[69]
Christo et al.[70]
Feng et al.[71]
Xu et al.[72]
Chen et al.[73]
Chen et al.[74]
Wang et al.[75]
Wang et al.[76]
Liu et al.[77] Encrypted health record and the extraction signature
Theodouli et al.[78] Health record
Badr et al.[79]
Nguyen et al.[80]
Guo et al.[81]
Nguyen et al.[82]
Liang et al.[83]
Iqbal et al.[84]
Dwivedi et al.[85]
Uddin et al.[86]
Xia et al.[87]
Steinfeld et al.[88]

Of the 24 papers reviewed and listed in Table 3, twelve stated they store the encrypted health records data in the cloud database, whereas one stores the extraction signature along with the encrypted health records data, and eleven store the clear health records data in the cloud. Extraction signature is the one generated for the health records data after removing the sensitive information from the originally signed data.[88] Regarding the blockchain transaction, some works include the hash of the health records that are stored in the cloud database as transaction payload. This ensures security in terms of data integrity because any modification to the record will result in a new hash value that will be different from the one stored in the blockchain. Other works record either data update and/or query events to the cloud database as transactions in the blockchain. It is crucial to record the cloud data update and query events as blockchain transactions to ensure the privacy of health records because any malicious access to the database will be logged and audit-trailed. Consequently, this discourages malevolent activities. However, very few works[66][75][83][85] consider security and privacy in their non-encapsulated BcC architecture. In addition, only eight include the access control policy in the blockchain transactions for authorized cloud data access (Table 3).

In summary, non-encapsulated BcC architecture is suitable for healthcare applications as it is more secure and private compared to encapsulated architecture. The patients’ medical records are stored in the cloud, but the blockchain is implemented outside the cloud, and each healthcare stakeholder owns a copy of the ledger that consists of the medical metadata, leading to a secure and private healthcare system. In this architecture, the system is upgraded by the cloud service provider hosting the health records. However, in both encapsulated and non-encapsulated architectures, the patients’ medical records are stored in the third-party cloud database, which might delay patient treatment as the data is not locally available to allied health professionals.

Table 4 shows the strengths and weaknesses of cloud-based, blockchain-based, and integrated BcC healthcare systems. It shows whether or not these systems satisfy security, privacy, scalability, and real-time data access requirements. The elastic and dynamic characteristics of a cloud-based system offer scalability, but the system suffers from the issues of security, privacy, and real-time data access. A blockchain-based system ensures security, privacy, and real-time data access (from the local copy of the ledger), but it is not scalable. The encapsulated BcC system offers scalability, as the blockchain is implemented within the cloud. However, cloud storage suffers from security, privacy, and real-time data access issues. The non-encapsulated BcC system is secure, private, and scalable. However, as the health records are stored in the cloud, real-time data access is an issue.

Table 4. Strengths and weaknesses of cloud-based, blockchain-based and integrated BcC healthcare systems
 
Security: ✓→ the system ensures data integrity and ✗→ the system does not ensure data integrity. Privacy: ✓→ the system ensures data privacy and ✗→ the system does not ensure data privacy. Scalability: ✓→ the system scales when the number of nodes increases and ✗→ the system does not scale. Real-time data access: ✓→ the system allows real-time access of health records and ✗→ the system does not allow real-time access of health records.
Healthcare system Security Privacy Scalability Real-time data access Remarks
Cloud-based The system scales but suffers from security and privacy issues. Health records can not be accessed in real-time as they are stored in the cloud.
Blockchain-based The system ensures security and privacy, and enables real-time data access of the health records from the local copy of the ledger. However, it does not scale.
Integrated BcC - Encapsulated The system scales but suffers from security and privacy issues. The health records can not be accessed in real-time as they are stored in the cloud.
Integrated BcC - Non-encapsulated The system scales and ensures security and privacy. The health records can not be accessed in real-time as they are stored in the cloud.

Healthcare applications

An integrated BcC healthcare system has been proposed for different healthcare applications, including health records management[44][45][46][47][48][49][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88], health data analytics[80], audit trail[89], supply chain management[90], and medical research[50], as shown in Figure 6.


Fig6 Ismail Sensors21 21-11.png

Figure 6. Integrated BcC opportunities in healthcare

The healthcare records management systems proposed by the authors of cited works 44–49 are based on encapsulated BcC architecture, whereas the ones proposed by the authors of cited works 65–88 are based on non-encapsulated architecture. Nguyen et al.[80] propose a mobile cloud-based "internet of medical things" framework using blockchain for automated health assessment. The authors use an integrated non-encapsulated BcC system for health data analytics to monitor the progression of neurological disorders. The data stored in the cloud can be accessed through blockchain smart contracts. Zhu et al.[89] proposed a non-encapsulated BcC architecture for the audit trail of healthcare services. The system allows patients to request a paid healthcare service to the cloud. After the end of the service, the patient rates the service provider, and these ratings are stored in the blockchain for audit trail. Celiz et al.[90] propose non-encapsulated healthcare system architecture for supply chain management to track the manufacturing of medical drugs. The data generated using the manufacturing process is stored in the cloud, and the data generation events are recorded in the blockchain. Smart contracts are used to ensure the efficient delivery of medical drugs from the source to the destination. For instance, if the shipment is delivered late, then a penalty will be imposed on the supplier using smart contracts. Park et al.[50] proposed an encapsulated BcC architecture for medical research. The authors use blockchain to securely collect and record the evaluation of healthcare remedies such as foods and dietary supplements from different participants. The system uses a cryptocurrency-based reward system to motivate voluntary participation. The proposed system is implemented in the cloud to enhance scalability.

Integrated BcC architecture: Research challenges and possible solutions

Scalability

Scalability of the integrated BcC healthcare system architecture is a major issue as the number of health records is continuously increasing. The processing and storage of these records bottleneck system scalability. Compared to encapsulated architecture, non-encapsulated is more scalable as the health records are stored in the cloud database, and only the associated metadata is recorded as blockchain transactions. However, with the increasing number of participants (healthcare organizations), the scalability of the system is still questionable because of blockchain consensus and replication.

To improve the scalability of blockchain, The Zilliqa Team[91] proposed the concept of a sharding network. In this network, the blockchain nodes are divided into shards and the transactions are divided among the shards for parallel verification.[92] In 2019, Ismail et al.[43] proposed a lightweight blockchain architecture for healthcare where the network participants are divided into clusters based on geographical locations. Each cluster has a cluster head, and the ledger is only replicated on the cluster heads instead of all network participants. Consequently, the scalability of the system increases. However, none of these solutions has been evaluated and implemented for integrated BcC architecture.

Energy consumption

Energy consumption of the integrated BcC architecture is a major concern, as both cloud computing and blockchain are energy-intensive. The energy consumption of cloud computing is a well-known research area, and several works in the literature have proposed solutions for energy-efficient cloud computing.[93][94] Regarding blockchain, consensus algorithms consume a significant amount of energy. Several compute-intensive, capability-based, and voting-based consensus algorithms have been proposed for blockchain in the literature.[22] The compute-intensive algorithms such as Proof of Work (PoW) consume a high amount of energy. To address the issue of energy consumption of compute-intensive algorithms, voting-based algorithms such as Practical Byzantine Fault Tolerance (PBFT) are used. However, PBFT involves a high number of message transfers, which might limit the scalability of the system, in particular for the healthcare domain. Moreover, no work examines the communication energy consumption of PBFT due to a high number of message transfers. The high energy consumption of an integrated BcC leads to environmental hazards such as global warming and carbon footprints.[95] Consequently, it is important to have research in this direction.

Possible research directions could look to improve the hardware characteristics of cloud resources to make them more energy-efficient and to develop energy-efficient consensus mechanisms such as the cuckoo hash PoW.[96] However, to reduce the environmental impact of these energy-hungry technologies, research should focus on the adaptation of sustainable energy sources such as wind, hydro, or solar.

Interoperability

With an increasing trend towards integrated BcC healthcare system architecture, multiple healthcare organizations are using BaaS. To have a cohesive view of a patient’s medical history, all the involved healthcare organizations are thus required to adhere to the same cloud provider. If the healthcare organizations are associated with different cloud service providers, then these service providers must collaborate to use the potential benefits of the integrated BcC architecture. The cloud interoperability standards developed by the National Institute of Standards and Technology (NIST) should be followed by different service providers to support inter-cloud communication.[97] Cloud providers should formalize cooperation agreements based on different levels of interoperability. For instance, the eHealth European Interoperability Framework (eHealth EIF) can be used as a baseline to define and develop legal, organizational, semantic, and technical interoperability levels.[98] However, the eHealth EIF framework should be further expanded for Integrated BcC architecture.

Real-time data access

Real-time access to patients’ medical records is crucial for healthcare providers, as delay in data access might lead to a patient’s death. In both encapsulated and non-encapsulated architectures, medical records are stored with a third-party cloud service provider, and there is no local copy of the data with healthcare professionals. This might lead to a delay in accessing the records depending on the network. Research efforts are required for ensuring real-time data access. One potential solution could be caching delay-sensitive health data, such as medication, allergies, results, and treatment plans, in the proximity of healthcare providers for real-time data access in life-threatening situations. Cloud architecture can be expanded by introducing intermediate layer(s) for data storage, such as edge and fog, between the cloud providers and the cloud consumers, i.e., patients, allied health professionals, and health organizations. The inclusion of an edge or fog layer can aid in real-time data access in a cloud-based system.[99]

Discussion

This paper investigated the current research on integrated BcC healthcare system architecture for more efficient and accurate patient care. Cloud technology offers scalability and reduced total cost of ownership, while blockchain technology promises a trustless decentralized, secure, and private environment. The main objective of this paper is to identify the current state of the art in the integrated BcC architecture and to present the strengths and weaknesses of this architecture.

When implementing an encapsulated or non-encapsulated architecture, the following requirements should be considered:

  • Security: With an encapsulated architecture, the blockchain is encapsulated within the cloud and the underlying blockchain technology is implemented by the cloud service provider. Consequently, the healthcare stakeholders have to trust the cloud service provider for data security, as the cloud service provider might tamper with the patients’ records by modifying the underlying blockchain implementation. In this case, an integrated BcC healthcare system is similar to a standalone cloud-based healthcare system. On the other hand, in the case of non-encapsulated architecture, the patients’ records are stored in the cloud database, whereas the blockchain is implemented outside the cloud with each stakeholder having a copy of the ledger. The ledger includes health records' metadata. Stakeholders can track any changes in health records by the cloud service provider. Therefore, non-encapsulated architecture addresses the issue of data security in healthcare.
  • Privacy: With encapsulated architecture, the privacy threat still exists as the cloud service provider might use the patient’s record without the patient’s knowledge. The data query transaction in the blockchain can be removed from the ledger by the cloud service provider, as the provider is the one who implements the blockchain and holds the copy of the ledger. On the other hand, in the case of non-encapsulated architecture, the privacy of health records is preserved because the blockchain is implemented outside the cloud, and each stakeholder owns a copy of the ledger. Any data query will be recorded in the blockchain ledger, thus making the healthcare system private. Consequently, non-encapsulated architecture addresses the issue of privacy in healthcare.
  • Medical records destruction: With encapsulated architecture, health records are stored in the blockchain ledger and replicated across different network participants. The records stored in the ledger cannot be destroyed because of blockchain's immutability characteristics. Any attempt to destroy the records will be logged in the ledger. On the other hand, in the case of non-encapsulated architecture, health records are stored in the cloud database and not replicated in the ledger. Only the hash of these records and the query and update events are logged in the ledger. Consequently, the destruction of records is possible. The records stored in the cloud database can be destroyed and the destruction event will be stored in the ledger.
  • Total cost of ownership: Table 5 shows that healthcare organizations which implement a non-encapsulated architecture incur the extra cost of recruiting onsite blockchain developers, compared to encapsulated architecture.
Table 5. Cost for encapsulated and non-encapsulated BcC architectures
 
* Prices are based on Microsoft Azure Blockchain as a Service for U.S. East deployment.[52] ** Price is for Microsoft surface laptop (8GB RAM and 128GB SSD)[100] based on Bitcoin node requirement.[101] *** Price is based on Microsoft Azure data lake storage.[102] **** The price is based on annual energy consumption of 63 terawatt-hours by a Bitcoin node.[103][104] The average electricity rate of 13.19 cents per kilowatt hour (kWh) in the United States is considered.[105]
BcC architecture Consideration Cost
Encapsulated Node $2,785.68* /year
Transaction $0.0001* (50 transactions/day are not charged)
Cloud storage (ledger) $0.6* /GB/year
Encapsulated Node ≈$1,000** (Four years maintenance)
Cloud storage (health records) $0.00972*** /GB/year
Blockchain developer $136,000/year[106]
Operation (energy) $8,309.70**** /year

Conclusions

In this paper, we highlight the importance of an integrated BcC healthcare system and present a taxonomy of BcC architectures. We also compare the BaaS platform offered by different cloud service providers. Additionally, we highlight the issues existing in the integrated architecture and present possible solutions for future research directions. In summary, the integration of cloud and blockchain for healthcare is promising to cope with the shortcomings of these individual technologies. Further research is still required to enhance the existing architecture to make it more scalable and energy-efficient with inter-cloud communication support.

Acknowledgements

Author contributions

Conceptualization, L.I.; methodology, L.I.; investigation, L.I., H.M. and A.H.; writing—original draft preparation, L.I., H.M. and A.H.; writing—review and editing, L.I. and H.M.; supervision, L.I.; project administration, L.I.; funding acquisition, L.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Water and Energy Center of the United Arab Emirates under grant number 31R215.

Conflicts of interest

The authors declare no conflict of interest.

References

  1. Ismail, Leila; Materwala, Huned; Karduck, Achim P; Adem, Abdu (7 July 2020). "Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review" (in en). Journal of Medical Internet Research 22 (7): e17508. doi:10.2196/17508. ISSN 1438-8871. PMC PMC7380987. PMID 32348265. https://www.jmir.org/2020/7/e17508. 
  2. Rind, D. M.; Kohane, I. S.; Szolovits, P.; Safran, C.; Chueh, H. C.; Barnett, G. O. (15 July 1997). "Maintaining the confidentiality of medical records shared over the Internet and the World Wide Web". Annals of Internal Medicine 127 (2): 138–141. doi:10.7326/0003-4819-127-2-199707150-00008. ISSN 0003-4819. PMID 9230004. https://pubmed.ncbi.nlm.nih.gov/9230004. 
  3. Schoenberg, R.; Safran, C. (11 November 2000). "Internet based repository of medical records that retains patient confidentiality". BMJ (Clinical research ed.) 321 (7270): 1199–1203. doi:10.1136/bmj.321.7270.1199. ISSN 0959-8138. PMC 1118958. PMID 11073513. https://pubmed.ncbi.nlm.nih.gov/11073513. 
  4. Uckert, Frank; Görz, Michael; Ataian, Maximilian; Prokosch, Hans-Ulrich (2002). "Akteonline-an electronic healthcare record as a medium for information and communication". Studies in Health Technology and Informatics 90: 293–297. ISSN 0926-9630. PMID 15460705. https://pubmed.ncbi.nlm.nih.gov/15460705. 
  5. Grant, Richard W.; Wald, Jonathan S.; Poon, Eric G.; Schnipper, Jeffrey L.; Gandhi, Tejal K.; Volk, Lynn A.; Middleton, Blackford (1 October 2006). "Design and Implementation of a Web-Based Patient Portal Linked to an Ambulatory Care Electronic Health Record: Patient Gateway for Diabetes Collaborative Care" (in en). Diabetes Technology & Therapeutics 8 (5): 576–586. doi:10.1089/dia.2006.8.576. ISSN 1520-9156. PMC PMC3829634. PMID 17037972. http://www.liebertpub.com/doi/10.1089/dia.2006.8.576. 
  6. Gritzalis, Dimitris; Lambrinoudakis, Costas (1 March 2004). "A security architecture for interconnecting health information systems" (in en). International Journal of Medical Informatics 73 (3): 305–309. doi:10.1016/j.ijmedinf.2003.12.011. https://linkinghub.elsevier.com/retrieve/pii/S1386505603002144. 
  7. Ismail, Leila; Materwala, Huned (22 June 2020). "BlockHR: A Blockchain-based Framework for Health Records Management" (in en). Proceedings of the 12th International Conference on Computer Modeling and Simulation (Brisbane QLD Australia: ACM): 164–168. doi:10.1145/3408066.3408106. ISBN 978-1-4503-7703-4. https://dl.acm.org/doi/10.1145/3408066.3408106. 
  8. Ismail, Leila; Materwala, Huned; Khan, Moien AB (8 July 2020). "Performance Evaluation of a Patient-Centric Blockchain-based Healthcare Records Management Framework" (in en). Proceedings of the 2020 2nd International Electronics Communication Conference (Singapore Singapore: ACM): 39–50. doi:10.1145/3409934.3409941. ISBN 978-1-4503-7770-6. https://dl.acm.org/doi/10.1145/3409934.3409941. 
  9. Chang, P. Y.; Bjornstad, K. A.; Rosen, C. J.; McNamara, M. P.; Mancini, R.; Goldstein, L. E.; Chylack, L. T.; Blakely, E. A. (1 October 2005). "Effects of Iron Ions, Protons and X Rays on Human Lens Cell Differentiation" (in en). Radiation Research 164 (4): 531–539. doi:10.1667/RR3368.1. ISSN 0033-7587. http://www.bioone.org/doi/10.1667/RR3368.1. 
  10. 10.0 10.1 Mell, P.; Grance, T. (September 2011). "The NIST Definition of Cloud Computing". National Institute of Standards and Technology. http://faculty.winthrop.edu/domanm/csci411/Handouts/NIST.pdf. Retrieved 27 May 2021. 
  11. 11.0 11.1 Foote, E.; Montalto M. (21 October 2019). "How Cloud EHR Reduces Operating Costs, Increases Computing Power". EHR Intelligence. XTelligent Healthcare Media. https://ehrintelligence.com/news/how-cloud-ehr-reduces-operating-costs-increases-computing-power. Retrieved 27 May 2021. 
  12. Pfitzmann, Andreas; Köhntopp, Marit (2001), Federrath, Hannes, ed., "Anonymity, Unobservability, and Pseudonymity — A Proposal for Terminology", Designing Privacy Enhancing Technologies (Berlin, Heidelberg: Springer Berlin Heidelberg) 2009: 1–9, doi:10.1007/3-540-44702-4_1, ISBN 978-3-540-41724-8, http://link.springer.com/10.1007/3-540-44702-4_1. Retrieved 2021-10-29 
  13. 13.0 13.1 Azaria, Asaph; Ekblaw, Ariel; Vieira, Thiago; Lippman, Andrew (1 August 2016). "MedRec: Using Blockchain for Medical Data Access and Permission Management". 2016 2nd International Conference on Open and Big Data (OBD) (Vienna, Austria: IEEE): 25–30. doi:10.1109/OBD.2016.11. ISBN 978-1-5090-4054-4. http://ieeexplore.ieee.org/document/7573685/. 
  14. 14.0 14.1 Dagher, Gaby G.; Mohler, Jordan; Milojkovic, Matea; Marella, Praneeth Babu (1 May 2018). "Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology" (in en). Sustainable Cities and Society 39: 283–297. doi:10.1016/j.scs.2018.02.014. https://linkinghub.elsevier.com/retrieve/pii/S2210670717310685. 
  15. 15.0 15.1 Li, Hongyu; Zhu, Liehuang; Shen, Meng; Gao, Feng; Tao, Xiaoling; Liu, Sheng (1 August 2018). "Blockchain-Based Data Preservation System for Medical Data" (in en). Journal of Medical Systems 42 (8): 141. doi:10.1007/s10916-018-0997-3. ISSN 0148-5598. http://link.springer.com/10.1007/s10916-018-0997-3. 
  16. 16.0 16.1 Fan, Kai; Wang, Shangyang; Ren, Yanhui; Li, Hui; Yang, Yintang (1 August 2018). "MedBlock: Efficient and Secure Medical Data Sharing Via Blockchain" (in en). Journal of Medical Systems 42 (8): 136. doi:10.1007/s10916-018-0993-7. ISSN 0148-5598. http://link.springer.com/10.1007/s10916-018-0993-7. 
  17. 17.0 17.1 Dey, Tushar; Jaiswal, Shaurya; Sunderkrishnan, Shweta; Katre, Neha (1 December 2017). "HealthSense: A medical use case of Internet of Things and blockchain". 2017 International Conference on Intelligent Sustainable Systems (ICISS) (Palladam: IEEE): 486–491. doi:10.1109/ISS1.2017.8389459. ISBN 978-1-5386-1959-9. https://ieeexplore.ieee.org/document/8389459/. 
  18. Jamil, Faisal; Hang, Lei; Kim, KyuHyung; Kim, DoHyeun (7 May 2019). "A Novel Medical Blockchain Model for Drug Supply Chain Integrity Management in a Smart Hospital" (in en). Electronics 8 (5): 505. doi:10.3390/electronics8050505. ISSN 2079-9292. https://www.mdpi.com/2079-9292/8/5/505. 
  19. Jayaraman, Raja; Salah, Khaled; King, Nelson (1 April 2019). "Improving Opportunities in Healthcare Supply Chain Processes via the Internet of Things and Blockchain Technology:" (in en). International Journal of Healthcare Information Systems and Informatics 14 (2): 49–65. doi:10.4018/IJHISI.2019040104. ISSN 1555-3396. http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2019040104. 
  20. He, Xinchi; Alqahtani, Sarra; Gamble, Rose (1 July 2018). "Toward Privacy-Assured Health Insurance Claims". 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (Halifax, NS, Canada: IEEE): 1634–1641. doi:10.1109/Cybermatics_2018.2018.00273. ISBN 978-1-5386-7975-3. https://ieeexplore.ieee.org/document/8726522/. 
  21. Ismail, Leila; Zeadally, Sherali (1 July 2021). "Healthcare Insurance Frauds: Taxonomy and Blockchain-Based Detection Framework (Block-HI)". IT Professional 23 (4): 36–43. doi:10.1109/MITP.2021.3071534. ISSN 1520-9202. https://ieeexplore.ieee.org/document/9520214/. 
  22. 22.0 22.1 22.2 22.3 22.4 Ismail; Materwala (24 September 2019). "Article A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions" (in en). Symmetry 11 (10): 1198. doi:10.3390/sym11101198. ISSN 2073-8994. https://www.mdpi.com/2073-8994/11/10/1198. 
  23. Bordel, Borja; Alcarria, Ramon; Martin, Diego; Sánchez-Picot, Álvaro (2018). "Trust Provision in the Internet of Things using Transversal Blockchain Networks" (in en). Intelligent Automation and Soft Computing 25 (1): 155–70. doi:10.31209/2018.100000052. 
  24. Le Nguyen, Bao; Laxmi Lydia, E.; Elhoseny, Mohamed; V. Pustokhina, Irina; A. Pustokhin, Denis; Mohamed Selim, Mahmoud; Nhu Nguyen, Gia; Shankar, K. (2020). "Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data" (in en). Computers, Materials & Continua 65 (1): 87–107. doi:10.32604/cmc.2020.011599. ISSN 1546-2226. https://www.techscience.com/cmc/v65n1/39555. 
  25. 25.0 25.1 Ismail, Leila; Materwala, Huned (22 July 2020). "Blockchain Paradigm for Healthcare: Performance Evaluation" (in en). Symmetry 12 (8): 1200. doi:10.3390/sym12081200. ISSN 2073-8994. https://www.mdpi.com/2073-8994/12/8/1200. 
  26. 26.0 26.1 Hern, A. (27 November 2017). "Bitcoin mining consumes more electricity a year than Ireland". The Guardian. https://www.theguardian.com/technology/2017/nov/27/bitcoin-mining-consumes-electricity-ireland. Retrieved 27 May 2021. 
  27. 27.0 27.1 "Bitcoin Energy Consumption Index". Digiconomist. 2021. https://digiconomist.net/bitcoin-energy-consumption. Retrieved 27 May 2021. 
  28. 28.0 28.1 28.2 Hu, Yan; Bai, Guohua (30 November 2014). "A Systematic Literature Review of Cloud Computing in Ehealth". Health Informatics - An International Journal 3 (4): 11–20. doi:10.5121/hiij.2014.3402. http://www.airccse.org/journal/hiij/papers/3414hiij02.pdf. 
  29. 29.0 29.1 29.2 Ali, Omar; Shrestha, Anup; Soar, Jeffrey; Wamba, Samuel Fosso (1 December 2018). "Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review" (in en). International Journal of Information Management 43: 146–158. doi:10.1016/j.ijinfomgt.2018.07.009. https://linkinghub.elsevier.com/retrieve/pii/S0268401218303736. 
  30. 30.0 30.1 30.2 Mehraeen, Esmaeil; Ghazisaeedi, Marjan; Farzi, Jebraeil; Mirshekari, Saghar (12 July 2016). "Security Challenges in Healthcare Cloud Computing: A Systematic Review". Global Journal of Health Science 9 (3): 157. doi:10.5539/gjhs.v9n3p157. ISSN 1916-9744. http://www.ccsenet.org/journal/index.php/gjhs/article/view/59729. 
  31. 31.0 31.1 31.2 Ermakova, T.; Huenges, J.; Erek, K. et al. (2013). "Cloud Computing in Healthcare – a Literature Review on Current State of Research". AMCIS 2013 Proceedings: 1–9. ISBN 9780615559070. https://aisel.aisnet.org/amcis2013/HealthInformation/GeneralPresentations/17/. 
  32. 32.0 32.1 32.2 Hölbl, Marko; Kompara, Marko; Kamišalić, Aida; Nemec Zlatolas, Lili (10 October 2018). "A Systematic Review of the Use of Blockchain in Healthcare" (in en). Symmetry 10 (10): 470. doi:10.3390/sym10100470. ISSN 2073-8994. http://www.mdpi.com/2073-8994/10/10/470. 
  33. 33.0 33.1 33.2 Kuo, Tsung-Ting; Zavaleta Rojas, Hugo; Ohno-Machado, Lucila (1 May 2019). "Comparison of blockchain platforms: a systematic review and healthcare examples" (in en). Journal of the American Medical Informatics Association 26 (5): 462–478. doi:10.1093/jamia/ocy185. ISSN 1527-974X. PMC PMC7787359. PMID 30907419. https://academic.oup.com/jamia/article/26/5/462/5419321. 
  34. 34.0 34.1 34.2 Agbo, Cornelius; Mahmoud, Qusay; Eklund, J. (4 April 2019). "Blockchain Technology in Healthcare: A Systematic Review" (in en). Healthcare 7 (2): 56. doi:10.3390/healthcare7020056. ISSN 2227-9032. PMC PMC6627742. PMID 30987333. https://www.mdpi.com/2227-9032/7/2/56. 
  35. 35.0 35.1 35.2 Vazirani, Anuraag A; O'Donoghue, Odhran; Brindley, David; Meinert, Edward (12 February 2019). "Implementing Blockchains for Efficient Health Care: Systematic Review" (in en). Journal of Medical Internet Research 21 (2): e12439. doi:10.2196/12439. ISSN 1438-8871. PMC PMC6390185. PMID 30747714. http://www.jmir.org/2019/2/e12439/. 
  36. 36.0 36.1 36.2 Hussien, H. M.; Yasin, S. M.; Udzir, S. N. I.; Zaidan, A. A.; Zaidan, B. B. (1 October 2019). "A Systematic Review for Enabling of Develop a Blockchain Technology in Healthcare Application: Taxonomy, Substantially Analysis, Motivations, Challenges, Recommendations and Future Direction" (in en). Journal of Medical Systems 43 (10): 320. doi:10.1007/s10916-019-1445-8. ISSN 0148-5598. http://link.springer.com/10.1007/s10916-019-1445-8. 
  37. Bahga, Arshdeep; Madisetti, Vijay K. (1 September 2013). "A Cloud-based Approach for Interoperable Electronic Health Records (EHRs)". IEEE Journal of Biomedical and Health Informatics 17 (5): 894–906. doi:10.1109/JBHI.2013.2257818. ISSN 2168-2194. http://ieeexplore.ieee.org/document/6497443/. 
  38. Fernández-Cardeñosa, Gonzalo; de la Torre-Díez, Isabel; López-Coronado, Miguel; Rodrigues, Joel J. P. C. (1 December 2012). "Analysis of Cloud-Based Solutions on EHRs Systems in Different Scenarios" (in en). Journal of Medical Systems 36 (6): 3777–3782. doi:10.1007/s10916-012-9850-2. ISSN 0148-5598. http://link.springer.com/10.1007/s10916-012-9850-2. 
  39. Zangara, Gianluca; Corso, Pietro Paolo; Cangemi, Francesco; Millonzi, Filippo; Collova, Francesco; Scarlatella, Antonio (2014). "A cloud based architecture to support Electronic Health Record". Innovation in Medicine and Healthcare 2014: 380–389. doi:10.3233/978-1-61499-474-9-380. https://ebooks.iospress.nl/doi/10.3233/978-1-61499-474-9-380. 
  40. Kupwade Patil, Harsh; Seshadri, Ravi (1 June 2014). "Big Data Security and Privacy Issues in Healthcare". 2014 IEEE International Congress on Big Data (Anchorage, AK: IEEE): 762–765. doi:10.1109/BigData.Congress.2014.112. ISBN 978-1-4799-5057-7. https://ieeexplore.ieee.org/document/6906856/. 
  41. Abbas, Assad; Khan, Samee U. (2015), Gkoulalas-Divanis, Aris; Loukides, Grigorios, eds., "e-Health Cloud: Privacy Concerns and Mitigation Strategies" (in en), Medical Data Privacy Handbook (Cham: Springer International Publishing): 389–421, doi:10.1007/978-3-319-23633-9_15, ISBN 978-3-319-23632-2, http://link.springer.com/10.1007/978-3-319-23633-9_15. Retrieved 2021-10-29 
  42. Nakamoto, S. (2008). "Bitcoin: A Peer-To-Peer Electronic Cash System". Bitcoin Project. https://bitcoin.org/bitcoin.pdf. Retrieved 27 May 2021. 
  43. 43.0 43.1 Ismail, Leila; Materwala, Huned; Zeadally, Sherali (2019). "Lightweight Blockchain for Healthcare". IEEE Access 7: 149935–149951. doi:10.1109/ACCESS.2019.2947613. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8869754/. 
  44. 44.0 44.1 Cao, Sheng; Zhang, Gexiang; Liu, Pengfei; Zhang, Xiaosong; Neri, Ferrante (1 June 2019). "Cloud-assisted secure eHealth systems for tamper-proofing EHR via blockchain" (in en). Information Sciences 485: 427–440. doi:10.1016/j.ins.2019.02.038. https://linkinghub.elsevier.com/retrieve/pii/S0020025519301471. 
  45. 45.0 45.1 Omar, Abdullah Al; Bhuiyan, Md Zakirul Alam; Basu, Anirban; Kiyomoto, Shinsaku; Rahman, Mohammad Shahriar (1 June 2019). "Privacy-friendly platform for healthcare data in cloud based on blockchain environment" (in en). Future Generation Computer Systems 95: 511–521. doi:10.1016/j.future.2018.12.044. https://linkinghub.elsevier.com/retrieve/pii/S0167739X18314201. 
  46. 46.0 46.1 Kurdi, Heba; Alsalamah, Shada; Alatawi, Asma; Alfaraj, Sara; Altoaimy, Lina; Ahmed, Syed Hassan (29 May 2019). "HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services" (in en). Electronics 8 (6): 602. doi:10.3390/electronics8060602. ISSN 2079-9292. https://www.mdpi.com/2079-9292/8/6/602. 
  47. 47.0 47.1 Kubendiran, Mohan; Singh, Satyapal; Sangaiah, Arun Kumar (30 April 2019). "Enhanced Security Framework for E-Health Systems using Blockchain". Journal of Information Processing Systems 15 (2): 239–250. doi:10.3745/JIPS.04.0106. https://doi.org/10.3745/JIPS.04.0106. 
  48. 48.0 48.1 Nagasubramanian, Gayathri; Sakthivel, Rakesh Kumar; Patan, Rizwan; Gandomi, Amir H.; Sankayya, Muthuramalingam; Balusamy, Balamurugan (1 February 2020). "Securing e-health records using keyless signature infrastructure blockchain technology in the cloud" (in en). Neural Computing and Applications 32 (3): 639–647. doi:10.1007/s00521-018-3915-1. ISSN 0941-0643. http://link.springer.com/10.1007/s00521-018-3915-1. 
  49. 49.0 49.1 Kaur, Harleen; Alam, M. Afshar; Jameel, Roshan; Mourya, Ashish Kumar; Chang, Victor (10 July 2018). "A Proposed Solution and Future Direction for Blockchain-Based Heterogeneous Medicare Data in Cloud Environment". Journal of Medical Systems 42 (8): 156. doi:10.1007/s10916-018-1007-5. ISSN 1573-689X. PMID 29987560. https://pubmed.ncbi.nlm.nih.gov/29987560. 
  50. 50.0 50.1 50.2 Park, Junseok; Park, Seongkuk; Kim, Kwangmin; Lee, Doheon (1 December 2018). "CORUS: Blockchain-Based Trustworthy Evaluation System for Efficacy of Healthcare Remedies". 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (Nicosia: IEEE): 181–184. doi:10.1109/CloudCom2018.2018.00044. ISBN 978-1-5386-7899-2. https://ieeexplore.ieee.org/document/8591013/. 
  51. Benet, J. (2014). "IPFS - Content Addressed, Versioned, P2P File System". arXiv. arXiv:1407.3561v1. https://arxiv.org/abs/1407.3561. 
  52. 52.0 52.1 52.2 "Blockchain". Azure. Microsoft. https://azure.microsoft.com/en-us/solutions/blockchain/. Retrieved 27 May 2021. 
  53. 53.0 53.1 "Blockchain on AWS". Amazon Web Services, Inc. https://aws.amazon.com/blockchain/. Retrieved 27 May 2021. 
  54. 54.0 54.1 "Oracle Blockchain". Oracle Corporation. https://www.oracle.com/ae/blockchain/. Retrieved 27 May 2021. 
  55. Gray, M. (9 November 2015). "Ethereum Blockchain as a Service now on Azure". Azure Blog. Microsoft. https://azure.microsoft.com/en-us/blog/ethereum-blockchain-as-a-service-now-on-azure/. Retrieved 27 May 2021. 
  56. "Amazon Managed Blockchain pricing". Amazon Web Services, Inc. https://aws.amazon.com/managed-blockchain/pricing/. Retrieved 27 May 2021. 
  57. Hyperledger (2020). "Channels". Hyperledger Fabric: A Blockchain Platform for the Enterprise. Read the Docs, Inc. https://hyperledger-fabric.readthedocs.io/en/release-2.2/channels.html. Retrieved 27 May 2021. 
  58. "Welcome to IBM Blockchain". IBM. https://www.ibm.com/ae-en/blockchain. Retrieved 27 May 2021. 
  59. Day, A. (13 June 2019). "Building hybrid blockchain/cloud applications with Ethereum and Google Cloud". Google Cloud. Google. https://cloud.google.com/blog/products/data-analytics/building-hybrid-blockchain-cloud-applications-with-ethereum-and-google-cloud. Retrieved 27 May 2021. 
  60. "SAP blockchain applications and services". SAP. https://www.sap.com/mena/products/intelligenttechnologies/blockchain.html. Retrieved 27 May 2021. 
  61. "Blockchain Solutions at Enterprise Scale". Hewlett Packard Enterprise Development LP. https://www.hpe.com/us/en/solutions/blockchain.html. Retrieved 27 May 2021. 
  62. "Blockchain as a Service". Alibaba Cloud. Alibaba. https://www.alibabacloud.com/product/baas. Retrieved 27 May 2021. 
  63. "Blockchain Service". Huawei Cloud. Huawei Services (Hong Kong) Co., Limited. https://www.huaweicloud.com/intl/en-us/product/bcs.html. Retrieved 27 May 2021. 
  64. XuperChain Lab. "XuperChain". GitHub. https://github.com/xuperchain/xuperchain. Retrieved 27 May 2021. 
  65. 65.0 65.1 65.2 Du, Yiwen; Liu, Jianwei; Guan, Zhenyu; Feng, Hanwen (1 September 2018). "A Medical Information Service Platform Based on Distributed Cloud and Blockchain". 2018 IEEE International Conference on Smart Cloud (SmartCloud) (New York, NY: IEEE): 34–39. doi:10.1109/SmartCloud.2018.00014. ISBN 978-1-5386-8000-1. https://ieeexplore.ieee.org/document/8513712/. 
  66. 66.0 66.1 66.2 66.3 Thwin, Thein Than; Vasupongayya, Sangsuree (1 August 2018). "Blockchain Based Secret-Data Sharing Model for Personal Health Record System". 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA) (Krabi: IEEE): 196–201. doi:10.1109/ICAICTA.2018.8541296. ISBN 978-1-5386-4804-9. https://ieeexplore.ieee.org/document/8541296/. 
  67. 67.0 67.1 67.2 Zheng, Xiaochen; Mukkamala, Raghava Rao; Vatrapu, Ravi; Ordieres-Mere, Joaqun (1 September 2018). "Blockchain-based Personal Health Data Sharing System Using Cloud Storage". 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) (Ostrava: IEEE): 1–6. doi:10.1109/HealthCom.2018.8531125. ISBN 978-1-5386-4294-8. https://ieeexplore.ieee.org/document/8531125/. 
  68. 68.0 68.1 68.2 Rouhani, Sara; Butterworth, Luke; Simmons, Adam D.; Humphery, Darryl G.; Deters, Ralph (1 July 2018). "MediChain TM : A Secure Decentralized Medical Data Asset Management System". 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (Halifax, NS, Canada: IEEE): 1533–1538. doi:10.1109/Cybermatics_2018.2018.00258. ISBN 978-1-5386-7975-3. https://ieeexplore.ieee.org/document/8726643/. 
  69. 69.0 69.1 69.2 Wang, Hao; Song, Yujiao (5 July 2018). "Secure Cloud-Based EHR System Using Attribute-Based Cryptosystem and Blockchain" (in en). Journal of Medical Systems 42 (8): 152. doi:10.1007/s10916-018-0994-6. ISSN 0148-5598. https://doi.org/10.1007/s10916-018-0994-6. 
  70. 70.0 70.1 70.2 Christo, Mary Subaja; A., Anigo Merjora; G., Partha Sarathy; C., Priyanka; M., Raj Kumari (1 April 2019). "An Efficient Data Security in Medical Report using Block Chain Technology". 2019 International Conference on Communication and Signal Processing (ICCSP) (Chennai, India: IEEE): 0606–0610. doi:10.1109/ICCSP.2019.8698058. ISBN 978-1-5386-7595-3. https://ieeexplore.ieee.org/document/8698058/. 
  71. 71.0 71.1 71.2 Feng, Tao; Jiao, Ying; Fang, Junli (2019), Wang, Xinheng; Gao, Honghao; Iqbal, Muddesar et al.., eds., "Secure Sharing Model Based on Block Chain in Medical Cloud (Short Paper)" (in en), Collaborative Computing: Networking, Applications and Worksharing (Cham: Springer International Publishing) 292: 429–438, doi:10.1007/978-3-030-30146-0_30, ISBN 978-3-030-30145-3, http://link.springer.com/10.1007/978-3-030-30146-0_30. Retrieved 2021-10-30 
  72. 72.0 72.1 72.2 Xu, Jie; Xue, Kaiping; Li, Shaohua; Tian, Hangyu; Hong, Jianan; Hong, Peilin; Yu, Nenghai (1 October 2019). "Healthchain: A Blockchain-Based Privacy Preserving Scheme for Large-Scale Health Data". IEEE Internet of Things Journal 6 (5): 8770–8781. doi:10.1109/JIOT.2019.2923525. ISSN 2327-4662. https://ieeexplore.ieee.org/document/8738810/. 
  73. 73.0 73.1 73.2 Chen, Lanxiang; Lee, Wai-Kong; Chang, Chin-Chen; Choo, Kim-Kwang Raymond; Zhang, Nan (1 June 2019). "Blockchain based searchable encryption for electronic health record sharing" (in en). Future Generation Computer Systems 95: 420–429. doi:10.1016/j.future.2019.01.018. https://linkinghub.elsevier.com/retrieve/pii/S0167739X18314134. 
  74. 74.0 74.1 74.2 Chen, Yi; Ding, Shuai; Xu, Zheng; Zheng, Handong; Yang, Shanlin (22 November 2018). "Blockchain-Based Medical Records Secure Storage and Medical Service Framework" (in en). Journal of Medical Systems 43 (1): 5. doi:10.1007/s10916-018-1121-4. ISSN 0148-5598. https://doi.org/10.1007/s10916-018-1121-4. 
  75. 75.0 75.1 75.2 75.3 Wang, Shangping; Zhang, Dan; Zhang, Yaling (2019). "Blockchain-Based Personal Health Records Sharing Scheme With Data Integrity Verifiable". IEEE Access 7: 102887–102901. doi:10.1109/ACCESS.2019.2931531. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8778648/. 
  76. 76.0 76.1 76.2 Wang, Yong; Zhang, Aiqing; Zhang, Peiyun; Wang, Huaqun (2019). "Cloud-Assisted EHR Sharing With Security and Privacy Preservation via Consortium Blockchain". IEEE Access 7: 136704–136719. doi:10.1109/ACCESS.2019.2943153. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8846684/. 
  77. 77.0 77.1 77.2 Liu, Jingwei; Li, Xiaolu; Ye, Lin; Zhang, Hongli; Du, Xiaojiang; Guizani, Mohsen (1 December 2018). "BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records". 2018 IEEE Global Communications Conference (GLOBECOM) (Abu Dhabi, United Arab Emirates: IEEE): 1–6. doi:10.1109/GLOCOM.2018.8647713. ISBN 978-1-5386-4727-1. https://ieeexplore.ieee.org/document/8647713/. 
  78. 78.0 78.1 78.2 Theodouli, Anastasia; Arakliotis, Stelios; Moschou, Konstantinos; Votis, Konstantinos; Tzovaras, Dimitrios (1 August 2018). "On the Design of a Blockchain-Based System to Facilitate Healthcare Data Sharing". 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (New York, NY, USA: IEEE): 1374–1379. doi:10.1109/TrustCom/BigDataSE.2018.00190. ISBN 978-1-5386-4388-4. https://ieeexplore.ieee.org/document/8456059/. 
  79. 79.0 79.1 79.2 Badr, Shaimaa; Gomaa, Ibrahim; Abd-Elrahman, Emad (2018). "Multi-tier Blockchain Framework for IoT-EHRs Systems" (in en). Procedia Computer Science 141: 159–166. doi:10.1016/j.procs.2018.10.162. https://linkinghub.elsevier.com/retrieve/pii/S187705091831812X. 
  80. 80.0 80.1 80.2 80.3 80.4 Nguyen, Dinh C.; Nguyen, Khoa D.; Pathirana, Pubudu N. (1 July 2019). "A Mobile Cloud based IoMT Framework for Automated Health Assessment and Management". 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Berlin, Germany: IEEE): 6517–6520. doi:10.1109/EMBC.2019.8856631. ISBN 978-1-5386-1311-5. https://ieeexplore.ieee.org/document/8856631/. 
  81. 81.0 81.1 81.2 Guo, Rui; Shi, Huixian; Zheng, Dong; Jing, Chunming; Zhuang, Chaoyuan; Wang, Zhengyang (2019). "Flexible and Efficient Blockchain-Based ABE Scheme With Multi-Authority for Medical on Demand in Telemedicine System". IEEE Access 7: 88012–88025. doi:10.1109/ACCESS.2019.2925625. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8750773/. 
  82. 82.0 82.1 82.2 Nguyen, Dinh C.; Pathirana, Pubudu N.; Ding, Ming; Seneviratne, Aruna (2019). "Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems". IEEE Access 7: 66792–66806. doi:10.1109/ACCESS.2019.2917555. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8717579/. 
  83. 83.0 83.1 83.2 83.3 Liang, Xueping; Zhao, Juan; Shetty, Sachin; Liu, Jihong; Li, Danyi (1 October 2017). "Integrating blockchain for data sharing and collaboration in mobile healthcare applications". 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (Montreal, QC: IEEE): 1–5. doi:10.1109/PIMRC.2017.8292361. ISBN 978-1-5386-3529-2. http://ieeexplore.ieee.org/document/8292361/. 
  84. 84.0 84.1 84.2 Iqbal, Jawaid; Umar, Arif Iqbal; Amin, Noorul; Waheed, Abdul (1 September 2019). "Efficient and secure attribute-based heterogeneous online/offline signcryption for body sensor networks based on blockchain" (in en). International Journal of Distributed Sensor Networks 15 (9): 155014771987565. doi:10.1177/1550147719875654. ISSN 1550-1477. http://journals.sagepub.com/doi/10.1177/1550147719875654. 
  85. 85.0 85.1 85.2 85.3 Dwivedi, Ashutosh Dhar; Srivastava, Gautam; Dhar, Shalini; Singh, Rajani (15 January 2019). "A Decentralized Privacy-Preserving Healthcare Blockchain for IoT". Sensors (Basel, Switzerland) 19 (2): 326. doi:10.3390/s19020326. ISSN 1424-8220. PMC 6359727. PMID 30650612. https://pubmed.ncbi.nlm.nih.gov/30650612. 
  86. 86.0 86.1 86.2 Uddin, Md. Ashraf; Stranieri, Andrew; Gondal, Iqbal; Balasubramanian, Venki (2018). "Continuous Patient Monitoring With a Patient Centric Agent: A Block Architecture". IEEE Access 6: 32700–32726. doi:10.1109/ACCESS.2018.2846779. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8383967/. 
  87. 87.0 87.1 87.2 Xia, Qi; Sifah, Emmanuel Boateng; Asamoah, Kwame Omono; Gao, Jianbin; Du, Xiaojiang; Guizani, Mohsen (2017). "MeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain". IEEE Access 5: 14757–14767. doi:10.1109/ACCESS.2017.2730843. ISSN 2169-3536. http://ieeexplore.ieee.org/document/7990130/. 
  88. 88.0 88.1 88.2 88.3 Steinfeld, Ron; Bull, Laurence; Zheng, Yuliang (2002), Kim, Kwangjo, ed., "Content Extraction Signatures", Information Security and Cryptology — ICISC 2001 (Berlin, Heidelberg: Springer Berlin Heidelberg) 2288: 285–304, doi:10.1007/3-540-45861-1_22, ISBN 978-3-540-43319-4, http://link.springer.com/10.1007/3-540-45861-1_22. Retrieved 2021-10-30 
  89. 89.0 89.1 Zhu, Xiaobao; Shi, Jing; Lu, Cuiyuan (23 July 2019). "Cloud Health Resource Sharing Based on Consensus-Oriented Blockchain Technology: Case Study on a Breast Tumor Diagnosis Service" (in en). Journal of Medical Internet Research 21 (7): e13767. doi:10.2196/13767. ISSN 1438-8871. PMC PMC6683652. PMID 31339106. http://www.jmir.org/2019/7/e13767/. 
  90. 90.0 90.1 Celiz, Rodrigo Cubas; De La Cruz, Yasmin Escriba; Sanchez, David Mauricio (1 November 2018). "Cloud Model for Purchase Management in Health Sector of Peru based on IoT and Blockchain". 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (Vancouver, BC: IEEE): 328–334. doi:10.1109/IEMCON.2018.8615063. ISBN 978-1-5386-7266-2. https://ieeexplore.ieee.org/document/8615063/. 
  91. The Zilliqa Team (May 2018). "The Zilliqa Project: A Secure, Scalable Blockchain Platform" (PDF). Zilliqa Research. https://docs.zilliqa.com/positionpaper.pdf. Retrieved 26 September 2020. 
  92. Frankenfield, J. (31 August 2018). "Sharding". Investopedia. Dotdash. https://www.investopedia.com/terms/s/sharding.asp. Retrieved 26 September 2020. 
  93. Ismail, Leila; Materwala, Huned (1 December 2018). "Energy-Aware VM Placement and Task Scheduling in Cloud-IoT Computing: Classification and Performance Evaluation". IEEE Internet of Things Journal 5 (6): 5166–5176. doi:10.1109/JIOT.2018.2865612. ISSN 2327-4662. https://ieeexplore.ieee.org/document/8437124/. 
  94. Ismail, Leila; Abed, Eyad H. (2019). "Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation". IEEE Access 7: 175003–175019. doi:10.1109/ACCESS.2019.2956881. ISSN 2169-3536. https://ieeexplore.ieee.org/document/8918076/. 
  95. Le, Kien; Bilgir, Ozlem; Bianchini, Ricardo; Martonosi, Margaret; Nguyen, Thu D. (12 June 2010). "Managing the cost, energy consumption, and carbon footprint of internet services" (in en). ACM SIGMETRICS Performance Evaluation Review 38 (1): 357–358. doi:10.1145/1811099.1811085. ISSN 0163-5999. https://dl.acm.org/doi/10.1145/1811099.1811085. 
  96. Tromp, J. (2015). "Cuckoo Cycle: A Memory Bound Graph-Theoretic Proof-of-Work". In Brenner, Michael; Christin, Nicolas; Johnson, Benjamin et al.. Financial cryptography and data security: FC 2015 international workshops, BITCOIN, WAHC and Wearable, San Juan, Puerto Rico, January 30, 2015: revised selected papers. Lecture notes in computer science Security and cryptology. Heidelberg New York, NY Dordrecht London: Springer. pp. 49–62. ISBN 978-3-662-48051-9. OCLC 912958705. https://www.worldcat.org/title/mediawiki/oclc/912958705. 
  97. Hogan M.; Liu, F.; Sokol, A. et al. (10 August 2011). "NIST-SP 500-291, NIST Cloud Computing Standards Roadmap". National Institute of Standards and Technology. https://www.nist.gov/publications/nist-sp-500-291-nist-cloud-computing-standards-roadmap. 
  98. Lamine, Elyes; Guédria, Wided; Rius Soler, Ariadna; Ayza Graells, Jordi; Fontanili, Franck; Janer-García, Léonard; Pingaud, Hervé (2 June 2017), Archimède, Bernard; Vallespir, Bruno, eds., "An Inventory of Interoperability in Healthcare Ecosystems: Characterization and Challenges" (in en), Enterprise Interoperability (Hoboken, NJ, USA: John Wiley & Sons, Inc.): 167–198, doi:10.1002/9781119407928.ch9, ISBN 978-1-119-40792-8, https://onlinelibrary.wiley.com/doi/10.1002/9781119407928.ch9. Retrieved 2021-11-01 
  99. Choy, Sharon; Wong, Bernard; Simon, Gwendal; Rosenberg, Catherine (1 October 2014). "A hybrid edge-cloud architecture for reducing on-demand gaming latency" (in en). Multimedia Systems 20 (5): 503–519. doi:10.1007/s00530-014-0367-z. ISSN 0942-4962. http://link.springer.com/10.1007/s00530-014-0367-z. 
  100. "Surface Laptop Go". Microsoft. https://www.microsoft.com/en-us/d/surface-laptop-go/94fc0bdgq7wv?activetab=pivot:overviewtab. Retrieved 19 May 2021. 
  101. Binns W. (31 December 2016). "Why is running a full bitcoin node important?". Updated Instructions: How to Run a Full Node. https://bitcoin.org/en/posts/how-to-run-a-full-node#why-is-running-a-full-bitcoin-node-important. Retrieved 19 May 2021. 
  102. "Azure Blob Storage pricing". Microsoft Azure. Microsoft. https://azure.microsoft.com/en-us/pricing/details/storage/. Retrieved 19 May 2021. 
  103. Bradbury, D. (8 May 2021). "How Much Power It Takes to Create a Bitcoin". The Balance. https://www.thebalance.com/how-much-power-does-the-bitcoin-network-use-391280. Retrieved 19 May 2021. 
  104. Canellis D. (6 May 2019). "Bitcoin has nearly 100,000 nodes, but over 50% run vulnerable code". The Next Web. https://thenextweb.com/news/bitcoin-100000-nodes-vulnerable-cryptocurrency. Retrieved 19 May 2021. 
  105. "See electric rates available to your home/business (updated today)". Electric Choice. Eisenbach Consulting, LLC. https://www.electricchoice.com/electricity-prices-by-state/. Retrieved 19 May 2021. 
  106. Sharma T.K.. "What is the salary for a blockchain developer in the USA?". Blockchain Council. https://www.blockchain-council.org/blockchain/what-is-the-salary-for-a-blockchain-developer-in-the-usa/. Retrieved 19 May 2021. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. In some cases important information was missing from the references, and that information was added. The original article pastes multiple URLs into the text body for some reason; for this version, most of those URLs were turned into formal citations, raising the citation count notably above the original 92. At the time of loading this article (30 October 2021) the link to SAP Cloud Platform Blockchain Service in the original article is dead, and a new URL could not be found; it's possible SAP discontinued its blockchain offering. The original article's citation #73 is said to be included in Table 3, but it unfortunately is not; that citation is removed for this version.