Security, Privacy, and Reliability in Digital Healthcare Systems Using Blockchain

Smart Contracts and Shared Platforms in Sustainable Health Care: Systematic Review

BACKGROUND

The benefits of smart contracts (SCs) for sustainable health care are a relatively recent topic that has gathered attention given its relationship with trust and the advantages of decentralization, immutability, and traceability introduced in health care. Nevertheless, more studies need to explore the role of SCs in this sector based on the frameworks propounded in the literature that reflect business logic that has been customized, automatized, and prioritized, as well as system trust. This study addressed this lacuna.

OBJECTIVE

This study aimed to provide a comprehensive understanding of SCs in health care based on reviewing the frameworks propounded in the literature.

METHODS

A structured literature review was performed based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles. One database-Web of Science (WoS)-was selected to avoid bias generated by database differences and data wrangling. A quantitative assessment of the studies based on machine learning and data reduction methodologies was complemented with a qualitative, in-depth, detailed review of the frameworks propounded in the literature.

RESULTS

A total of 70 studies, which constituted 18.7% (70/374) of the studies on this subject, met the selection criteria and were analyzed. A multiple correspondence analysis-with 74.44% of the inertia-produced 3 factors describing the advances in the topic. Two of them referred to the leading roles of SCs: (1) health care process enhancement and (2) assurance of patients' privacy protection. The first role included 6 themes, and the second one included 3 themes. The third factor encompassed the technical features that improve system efficiency. The in-depth review of these 3 factors and the identification of stakeholders allowed us to characterize the system trust in health care SCs. We assessed the risk of coverage bias, and good percentages of overlap were obtained-66% (49/74) of PubMed articles were also in WoS, and 88.3% (181/205) of WoS articles also appeared in Scopus.

CONCLUSIONS

This comprehensive review allows us to understand the relevance of SCs and the potentiality of their use in patient-centric health care that considers more than technical aspects. It also provides insights for further research based on specific stakeholders, locations, and behaviors.

SDACS: Blockchain-Based Secure and Dynamic Access Control Scheme for Internet of Things

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.

A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.

Towards enhancing security of IoT-Enabled healthcare system

The Internet-of-Things (IoT)-based healthcare systems are comprised of a large number of networked medical devices, wearables, and sensors that collect and transmit data to improve patient care. However, the enormous number of networked devices renders these systems vulnerable to assaults. To address these challenges, researchers advocated reducing execution time, leveraging cryptographic protocols to improve security and avoid assaults, and utilizing energy-efficient algorithms to minimize energy consumption during computation. Nonetheless, these systems still struggle with long execution times, assaults, excessive energy usage, and inadequate security. We present a novel whale-based attribute encryption scheme (WbAES) that empowers the transmitter and receiver to encrypt and decrypt data using asymmetric master key encryption. The proposed WbAES employs attribute-based encryption (ABE) using whale optimization algorithm behaviour, which transforms plain data to ciphertexts and adjusts the whale fitness to generate a suitable master public and secret key, ensuring security against unauthorized access and manipulation. The proposed WbAES is evaluated using patient health record (PHR) datasets collected by IoT-based sensors, and various attack scenarios are established using Python libraries to validate the suggested framework. The simulation outcomes of the proposed system are compared to cutting-edge security algorithms and achieved finest performance in terms of reduced 11 s of execution time for 20 sensors, 0.121 mJ of energy consumption, 850 Kbps of throughput, 99.85 % of accuracy, and 0.19 ms of computational cost.

Deep learning personalized recommendation-based construction method of hybrid blockchain model

This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

DRLBTS: deep reinforcement learning-aware blockchain-based healthcare system

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare  applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.

Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study

A challenging problem in millimeter wave (mmWave) communications for the fifth generation of cellular communications and beyond (5G/B5G) is the beam selection problem. This is due to severe attenuation and penetration losses that are inherent in the mmWave band. Thus, the beam selection problem for mmWave links in a vehicular scenario can be solved as an exhaustive search among all candidate beam pairs. However, this approach cannot be assuredly completed within short contact times. On the other hand, machine learning (ML) has the potential to significantly advance 5G/B5G technology, as evidenced by the growing complexity of constructing cellular networks. In this work, we perform a comparative study of using different ML methods to solve the beam selection problem. We use a common dataset for this scenario found in the literature. We increase the accuracy of these results by approximately 30%. Moreover, we extend the given dataset by producing additional synthetic data. We apply ensemble learning techniques and obtain results with about 94% accuracy. The novelty of our work lies in the fact that we improve the existing dataset by adding more synthetic data and by designing a custom ensemble learning method for the problem at hand.

Case studies about smart contracts in healthcare

The Internet of Things (IoT) such as devices and sensors are a fast growth reality which our bureaucratical and archaic institutional system is not yet ready to embrace its functionalities. In the health system, many developments are made, and smart devices are the key to preventing, studying, investigating, and solving a lot of diseases and improving our health system. But along with this, innovation is necessary for the hospitals, for example, to have a proper system that provides storage of health data information and respects the General Data Protection Regulation (GDPR) with the use of smart contracts that secure the integrity and disclosure of the patient's data, since the majority of hospitals still use paper, physical records to store data. In this study, we will briefly analyse and explain three different suggested methods to deal with the challenges that Internet of Medical Things (IoMT) encounters. We will not choose which one is the best because of the different features and the countries they are proposed but will emphasize the benefits and challenges which one has.

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

A Comprehensive Study of the Trends and Analysis of Distributed Ledger Technology and Blockchain Technology in the Healthcare Industry

In today’s scenario, it is essential for the healthcare sector to focus on balancing patient care records with information relevant to completeness, accessibility, and privacy concerns. Advancements in information technology and health infrastructure exponentially bolster transformative changes in the healthcare industry. Incorporation of blockchain along with distributed ledger technology (DLT) owes the potentiality to cater to the interoperability restraints in health IT systems and enables medical researchers, healthcare entities, and healthcare providers to share electronic health data in a secured and well-mannered system. In addition to these, such technologies also propose and offer latest models for health statistics exchange by making the records more secure and efficient. However, successful implementation of blockchain technology and DLT necessitates efficient infrastructure, connectivity, and other factors. Hence, there poses to be several challenges restraining the mainstream usage of blockchain technology in the healthcare sector. The article illustrates different generations of blockchain, issues in healthcare data, and network structures as well as the solutions offered by the sector to cater to such problems. In addition to these, the article also emphasizes on the different application areas of blockchain and DLT in healthcare infrastructure. This article further discusses latest trends and factors driving the need for the incorporation of blockchain and distributed ledger technology in the healthcare sector and the future scenario for the same.

Hierarchical Blockchain-Based Multi-Chaincode Access Control for Securing IoT Systems

The rapid growth of the Internet of Things (IoT) and its attributes of constrained devices and a distributed environment make it difficult to manage such a huge and growing network of devices on a global scale. Existing traditional access-control systems provide security and management to the IoT system. However, these mechanisms are based on central authority management, which introduces issues such as a single point of failure, low scalability, and a lack of privacy. In order to address these problems, many researchers have proposed using blockchain technology to achieve decentralized access control. However, such models are still faced with problems such as a lack of scalability and high computational complexity. In this paper, we propose a light-weight hierarchical blockchain-based multi-chaincode access control to protect the security and privacy of IoT systems. A clustering concept with BC managers enables the extended scalability of the proposed system. The architecture of the proposed solution contains three main components: an Edge Blockchain Manager (EBCM), which is responsible for authenticating and authorizing constrained devices locally; an Aggregated Edge Blockchain Manager (AEBCM), which contains various EBCMs to control different clusters and manage ABAC policies, and a Cloud Consortium Blockchain Manager (CCBCM), which ensures that only authorized users access the resources. In our solution, smart contracts are used to self-enforce decentralized AC policies. We implement a proof of concept for our proposed system using the permissioned Hyperledger Fabric. The simulation results and the security analysis show the efficiency and effectiveness of the proposed solution.

An AI-Enabled Hybrid Lightweight Authentication Model for Digital Healthcare Using Industrial Internet of Things Cyber-Physical Systems

In the era of smart healthcare, Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS) play an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks that need to be addressed to maintain credence among clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper we propose a lightweight hybrid deep learning protocol to achieve security and privacy. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overheads with the help of an ML technique to predicate and forward the authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation upshots of the ML scheme exhibited extraordinary security features with the cost-effective validation of legal patient wearable devices accompanied by worthwhile communication functionalities compared with previous work. However, the application of IoT-based medical devices and managing such a broad, sophisticated medical IoT system on standard Single Cloud platforms (CP) would be extremely tough. We propose a scalable FC with a blockchain-based architecture for a 5G-enabled IoMT platform. To work on an FC architecture with flowing effects, low overheads, and secure storage (SS), this research proposes a secured blockchain-based fogBMIoMT communication mechanism.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography

Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.

A Novel Secure Blockchain Framework for Accessing Electronic Health Records Using Multiple Certificate Authority

Blockchain is a promising technology in the context of digital healthcare systems, but there are issues related to the control of accessing the electronic health records. In this paper, we propose a novel framework based on blockchain and multiple certificate authority that implement smart contracts and access health records securely. Our proposed solution provides the facilities of flexible policies to update a record or invoke the policy such that a patient has complete authority. A novel approach towards multiple certificate’s authority (CA) is introduced in the design through our proposed framework. Our proposed policies and methods overcome the shortcoming and security breaches faced by single certificate authority. Our proposed scheme provides a flexible access control mechanism for securing electronic health records as compared to the existing benchmark models. Moreover, our proposed method provides a re-enrolment facility in the case of a user lost enrolment.

Reliable Internet of Things: Challenges and Future Trends

The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.