Assessing the Privacy Risks of Data Sharing in Genomics
Bibliographic record
Abstract
The protection of identity of participants in medical research has traditionally been guaranteed by the maintenance of the confidentiality of health information through mechanisms such as only releasing data in an aggregated form or after identifying variables have been removed. This protection of privacy is regarded as a fundamental principle of research ethics, through which the support of research participants and the public is maintained. Whilst this traditional model was adopted for genetics and genomics research, and was generally considered broadly fit for purpose, we argue that this approach is increasingly untenable in genomics. Privacy risk assessments need to have regard to the whole data environment, not merely the quality of the dataset to be released in isolation. As sources of data proliferate, issues of privacy protection are increasingly problematic in relation to the release of genomic data. However, we conclude that, by paying careful attention to potential pitfalls, scientific funders and researchers can take an important part in attempts to safeguard the public and ensure the continuation of potentially important scientific research.
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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 | 0.192 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".