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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".