Evidence map›Paper›PMID 34035885›Full record

ArticleJournal of healthcare engineering2021

Blockchain-Based Reversible Data Hiding for Securing Medical Images.

Ji-Hwei Horng, Ching-Chun Chang, Guan-Long Li, Wai-Kong Lee, Seong Oun Hwang

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ji-Hwei HorngDepartment of Electronic Engineering, National Quemoy University, Kinmen 89250, Taiwan.ORCID 0000-0002-2134-5257
Ching-Chun ChangDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, China.ORCID 0000-0001-7723-4591
Guan-Long LiDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.
Wai-Kong LeeDepartment of Computer Engineering, Gachon University, Republic of Korea.ORCID 0000-0003-4659-8979
Seong Oun HwangDepartment of Computer Engineering, Gachon University, Republic of Korea.ORCID 0000-0003-4240-6255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

Indexed as

BlockchainAlgorithmsElectronic Health RecordsHumans

Identifiers

PMID34035885
PMCPMC8123997

What OpenQuestion holds

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Registered trials

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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.