Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review

Rahmani, Mohammad Khalid Imam; Shuaib, Mohammed; Alam, Shadab; Siddiqui, Shams Tabrez; Ahmad, Sadaf; Bhatia, Surbhi; Mashat, Arwa

doi:10.1155/2022/9766844

Cited by 67 publications

(19 citation statements)

References 67 publications

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“…Reference [26] analyzed the possible privacy information leakage problem in STIX network security threat intelligence sharing standard, and tried to solve the privacy information leakage problem by using an improved data sharing protocol. The above research work has played a role in privacy protection from a unilateral dimension, but cannot protect the privacy information of the intelligence sharing party, the intelligence user and the intelligence-related parties at the same time [27][28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

A Secure Framework for Blockchain Transactions Protection

Al-Sharu¹,

Qabalin²,

Naser³

et al. 2023

Computer Systems Science and Engineering

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One of the most extensively used technologies for improving the security of IoT devices is blockchain technology. It is a new technology that can be utilized to boost the security. It is a decentralized peer-to-peer network with no central authority. Multiple nodes on the network mine or verify the data recorded on the Blockchain. It is a distributed ledger that may be used to keep track of transactions between several parties. No one can tamper with the data on the blockchain since it is unchangeable. Because the blocks are connected by hashes, the transaction data is safe. It is managed by a system that is based on the consensus of network users rather than a central authority. The immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and hashing. Furthermore, Blockchain has an immutable and tamper-proof smart contract, which is a logic that enforces the Blockchain's laws. There is a conflict between the privacy protection needs of cyber-security threat intelligent (CTI) sharing and the necessity to establish a comprehensive attack chain during blockchain transactions. This paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing parties. It builds a full attack chain using encrypted threat intelligence and exploits the blockchain's backtracking capacity to finish the decryption of the threat source in the attack chain. Smart contracts are also used to send automatic early warning replies to possible attack targets. Simulation tests are used to verify the feasibility and efficacy of the suggested model.

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Section: Related Workmentioning

confidence: 99%

A Secure Framework for Blockchain Transactions Protection

Al-Sharu¹,

Qabalin²,

Naser³

et al. 2023

Computer Systems Science and Engineering

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“…Deep learning models that take such trained word representations as input have been employed by researchers to classify unstructured texts documents [38], medical notes [39], health-related social media texts [40], and biomedical text mining tasks [41]. Besides these, handwritten script recognition [42], detection of diseases [43][44][45], and healthcare solutions [46] involve the potential application of deep learning models.…”

Section: Related Workmentioning

confidence: 99%

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Parwez

Fazil

Arif

et al. 2023

Computational Intelligence and Neuroscience

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Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

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“…REMOTE HEALTH MONITORING USING IOT WITH THREE SENSORS In this system, the use of a microcontroller is proposed along with the use of three basic sensors, including the pulse, ECG and temperature sensor. It proposes using IoT to send the data so that the details can be saved and processed on the cloud and alerts can be generated [13]. Additionally, since all this data is sensitive, user authentication is also employed for the security of the users.…”

Section: Analysis Of the Systemmentioning

confidence: 99%

Internet of Things-based Integrated Remote Electronic Health Surveillance and Alert System: A Review

Singh

Joshi

Alam

et al. 2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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The Internet of Things (IoT) has made it far simpler to implement a remote, real-time patient health monitoring system from locations other than hospitals, such as patients' homes. Sensors retrieve various data related to the patient and record a variety of indicators. Then, the IoT, which presents it on a website and provides access for remote monitoring, saves this information. Because of this, we can lessen the number of mistakes made by humans, and the amount of space that the system takes up in the room may be decreased due to its smaller size. The creation of alarms is employed in such a way that the patient is not prevented from taking their medications at the appropriate times. If the health parameters reach the predetermined value, a notification is usually delivered through email or a text message alert. The competent authorities can be aware of the situation thanks to the notification method. In this article, we have addressed the monitoring of several diseases and vital signs, including the rate of one's heartbeat, blood pressure, breathing rate, body temperature, and pace of movement, using various methods. Another critical aspect of the solution that has been presented is creating the ideal environment for the patient, considering their current state of health.

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Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review

Cited by 67 publications

References 67 publications

A Secure Framework for Blockchain Transactions Protection

A Secure Framework for Blockchain Transactions Protection

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Internet of Things-based Integrated Remote Electronic Health Surveillance and Alert System: A Review

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