An assessment of synthetic data generation, use and disclosure under Canadian privacy regulations
Bibliographic record
Abstract
Synthetic data generation (SDG) plays an increasingly important role as a research and innovation accelerator. While SDG can enable privacy-preserving data sharing, it also raises privacy concerns compounded by uncertainty how privacy law applies to SDG and the generated data itself. Such uncertainty can hinder positive applications of SDG and put individual privacy rights at risk. This study aims to understand how SDG and synthetic data are treated under Canadian federal privacy law, identifying regulatory gaps that extend beyond the Canadian context and proposing recommendations to address them. Our analysis shows that SDG is not explicitly addressed by the statute. While SDG arguably qualifies as a use of personal information, it is unclear whether consent is required for SDG. Further Fair Information Practices with respective obligations apply to SDG just as they do to any use of personal information. The generated data itself could fall outside the law's scope since it is more likely to qualify as non-personal than traditionally de-identified data but the concept of identifiability under the statute remains ambiguous, particularly regarding inferences. An unclear definition of identifiability represents a relevant gap in privacy law that can harm the individual directly, through the exposure of personal information, or indirectly, by hindering the adoption of SDG and other beneficial privacy-enhancing technologies. A Code of Practice, anchored in legislation, could address such privacy concerns and ensure the proper application of SDG.
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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 | 0.070 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".