DASS-CARE 2.0: Blockchain-Based Healthcare Framework for Collaborative Diagnosis in CIoMT Ecosystem

Ayache, Meryeme; Gawanmeh, Amjad; Al‐Karaki, Jamal N.

doi:10.1109/ciot53061.2022.9766532

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…The literature review is discussed in the related work section, where several existing models are there, and the results are compared in the final section. Ayache, M. et al [2], proposed a Decentralized Accessible Scalable and Secure (DASSCare 2.0) model that works on real-time health monitoring that route all the data from various resources and makes it available to various end users, which allows the collaborative health monitoring and maintains the bills paid by the patient. The model gives an extra edge to other frameworks.…”

Section: Related Workmentioning

confidence: 99%

“…Controlling who can and cannot access a system's resources is the major aspect of access control. Personal or medical health data [2] kept by many parties, and which may be required for third-party access to thirdparty aims (such as medical or insurance companies), is a difficult task to manage. The data collected from heterogeneous sources is difficult to manage for any insurance or medical organization.…”

Section: Introductionmentioning

confidence: 99%

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Attribute-based Access Control Model in Healthcare Systems with Blockchain Technology

Arora¹,

Bhagat²,

Kumar³

2023

IJACSA

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Blockchain and the healthcare sector have a serious concern with context to scalability, which has a challenge of converting arbitrary values to fixed values. The transfer of arbitrary data coming from diverse resources has another point of concern in the blockchain. In this paper, the author proposed a model that will receive data from diverse sources and will convert it to a fixed type of value. The paper also proposes an access control scheme with various permission and consensus level protocols which will allow a reduction in block size with respect to scalability. The consensus level will allow access to the individual or a group of users and the permission level with respect to each block via considering the access granted to nodes of the blockchain. The addition of various permission and consensus levels will allow only a restricted type of data to pass the model. Once the data is verified and approved by various levels, then the data is all set to be part of the blockchain. The paper introduces a model where the time taken to create a new hash is 0.15625 microseconds. A total number of 64 transactions taken from the data set where the throughput is calculated for individual access are considered. After applying the formula, the calculated throughput is 32.5 microseconds. By the lighter block size data can be made available to the patients. The research is for the patients so they can keep track of their medical history and the deaths due to overdose of the medicines can be reduced.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attribute-based Access Control Model in Healthcare Systems with Blockchain Technology

Arora¹,

Bhagat²,

Kumar³

2023

IJACSA

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An intelligent blockchain strategy for decentralised healthcare framework

Goel

Neduncheliyan

2023

Peer-to-Peer Netw. Appl.

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Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.

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