Motivation
IoMT has been adopted by healthcare departments rapidly, yet most devices are limited to resource constraints and security perspectives (Ghubaish et al., 2020). Classic e-healthcare systems are centric and have the inherent problems of single-point failure with low transparency and low control over records. Many proposals have been made in IoT to address the inadequate computing and storage of records through sensors. Benefits of adopting an e-healthcare system are in diagnoses and remotely accessing patient records through medical sensors (Cai et al., 2019). Such healthcare systems, however, are vulnerable to several cyber-security threats including, among other things, integrity and forging of records . Further, managing records and providing accurate decisions by sensors in intelligent healthcare systems is another challenging task. Since many security schemes have been discussed, providing efficient and secure healthcare with accuracy is still challenging. Further, the cloud-centric solutions where the record of a patient is transferred to the cloud for processing and analysis are not suitable for critical healthcare systems. A single-point failure or denial of service threat leads to the unavailability of resources and more delay in the system.
Blockchain technology is considered a decentralized distributed system that proves transparency and exhibits the traceability of records while sharing among entities (Agbo et al., 2019). Using blockchain technology in e-healthcare resolves several critical issues such as storage, accuracy, and forging of records issues, by providing high computation and scalability to the network (Hemalatha et al., 2023). The proposed mechanism focuses on the accuracy and transparency of records while sharing the information among entities. Further, it prevents the forging the information by intermediate entities that share or sell the medical records to third parties, such as life insurance companies or data analysts, without the patient's permission.
Issues Addressed in Existing Schemes
Using a hybrid mechanism, in this research paper we address the issues of:
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High latency while utilizing or analyzing the records.
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Using classical drawbacks such as centralized processing, storage, and the involvement of a third party to ensure entity authentication.
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Data integrity in a decentralized environment while managing and storing information in the network.
Challenges in Resolving the Problems
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The integration of blockchain technology in e-health-CPS is a complex task because of high-end resource requirements by the network.
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Storage of extensive blockchain data records may be considered a costly operation, which further involves high latency.
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The computation and communication time is significant while introducing trust-based mechanisms for providing accurate decisions.
Researchers have provided several secure and efficient communication and storage mechanisms. AI-based models, trust-based mechanisms, and cryptographic algorithms have been proposed. Existing schemes, however, still overlook several issues (Das et al., 2021; Hanif et al., 2022; Mansour et al., 2022; Nitaj & Rachidi, 2023). The critical issues that need to be focused on are the latency while processing or analyzing records, computation and communication overhead while including high-level security, single-point failure of storage using clouds, and cost. The proposed mechanism introduces a secure and efficient communication method that includes the computation of the trust of each communicating device before allowing it into the network. Further, blockchain is used to process the surveillance and transparency in the network.
Contribution of the Paper
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A novel hybrid architecture called zero trust blockchain architecture (ZTA) is proposed for decentralized e-health-CPS systems. This architecture supports low latency, records storage and processing, and patient monitoring.
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The proposed mechanism introduces selective record access control (RAC), which may further authenticate and anonymize patient records to support system privacy.
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The probability distribution function may further draft accurate, real-time monitoring of patient records while transmitting the information among entities in the network.
The motivation behind integrating ZTA and blockchain is to maintain a trusted and decentralized healthcare mechanism. Our proposal of enhancing the security and trust levels of tele-healthcare systems by using trust-driven blockchain is vital for the following reasons:
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Electronic medical records (EMR) can be stored and transmitted over the enhanced network securely to exchange information among multiple organizations.
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The service providers and seekers become accountable for their actions and behaviors.
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Records can be retrieved for legal requirements in the face of dispute.
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There is distributed surveillance of the devices’ activities to track the alteration and modification of information by intruders and evaluate trust of each participant.
In addition, the core strengths of blockchain are its high security, transparency, and immutability. To further bring in accountability, we conceptualized a mixed consortium-cum-public blockchain architecture with dynamic surveillance of the behavior of the nodes to assess their trust levels continuously. Further, we proposed a novel proof-of-trust consensus algorithm that allows only trusted nodes to participate in the process of block validation and block insertion. Incorporating periodic surveillance of node behavior and trust-based consensus will bring accountability and significantly reduce the long latency shown by proof-of-work of the order of several minutes to a few seconds.
The remaining structure of the paper is modeled as follows. Section two elaborates on the previous contribution of secure e-healthcare CPS by introducing several security schemes. Section three illustrates the proposed mechanism by detailing the zero trust blockchain architecture using the probability distribution function. Section four presents the cryptographic methods and logical analysis of a proposed model for validating and testing the results. Finally, section five concludes the paper and discusses future scope. The outline of the work is presented in Figure 2.