SecureConsent: A Blockchain-Based Dynamic and Secure Consent Management for Genomic Data Sharing
Bibliographic record
Abstract
The potential of precision oncology initiatives heavily relies on sharing and analyzing genomic data across diverse patient groups. However, the sensitivity of genomic data raises concerns about consent, and data control, hindering patient participation in such initiatives. Existing healthcare data-sharing methods are unable to fully address these issues, and they also do not take into account patient preferences and requirements within their models. Therefore in this paper, we introduce “SecureCon-sent”, a Patient-Centric consent management system designed for sharing genomic data. SecureConsent caters to patient preferences, allowing them to have control over how their genomic data is shared and used within precision oncology initiatives. It facilitates patients to make informed decisions and modify their consent at any moment through the implementation of dynamic informed consent. Moreover, it integrates a decentralized access control mechanism to establish a robust and patient-centric framework. SecureConsent also presents a user-friendly interface for simple interaction between patients and those requesting data. We conduct a performance evaluation of our blockchain-based model to establish its system efficiency, which includes analyzing gas cost, latency, and transaction throughput.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".