Evidence map›Paper›PMID 39992943›Full record

ArticlePLOS digital health2025

Validation and user experience testing of DataCryptChain: An open-source standard combining blockchain technology with asymmetric encryption for private, secure, shareable, and tamper-proof research data.

Jeffrey Michael Franc

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Jeffrey Michael FrancDepartment of Emergency Medicine, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0002-2421-3479

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DataCryptChain is an open-source standard combining blockchain with advanced encryption ensuring research data remains private, secure, shareable, and tamper-proof. Ability to detect intentional tampering of data was measured, and user experience was evaluated. In this study, simulated datasets were randomized to be tampered with or not tampered with, and detection of tampering was measured. Volunteer's ability to complete assigned tasks using the software was evaluated. Among 10000 simulated datasets (4436 randomized to tampering) there was 100% sensitivity and specificity for detection. All volunteers successfully installed DataCryptChain and 5/6 completed their tasks. All participants were able to transmit data without ever exposing unencrypted data and with no need to share passwords. Several deficiencies in the user experience were noted. Importantly, the test users felt that although they would be willing to use DataCryptChain in practice, it would need a more user-friendly interface. This study demonstrates a novel algorithm using blockchain and asymmetric encryption that, although previously documented theoretically, has never been published as a working software package. While DataCryptChain has 100% sensitivity and specificity for detecting data tampering, further development is needed to improve the user experience.

Identifiers

PMID39992943
PMCPMC11849895

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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.