Evidence map›Paper›PMID 37131047›Full record

ArticleScientific reports2023

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems.

Shitharth Selvarajan, Haralambos Mouratidis

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
31.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 68 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 2 countries.

Shitharth SelvarajanPost Doc Researcher (IADS), University of Essex, Colchester, UK. shitharths@kdu.edu.et.
Haralambos MouratidisInstitute for Analytics and Data Science (IADS), University of Essex, Colchester, England, UK.
University of Essex · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.

Indexed as

BlockchainComputer SecurityDelivery of Health CareElectronic Health RecordsHumansTrust

Identifiers

PMID37131047
PMCPMC10154383
OpenAlexW4367677400

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.