Adopt a PET! An Exploration of PETs, Policy, and Practicalities for Industry in Canada
Bibliographic record
Abstract
Privacy is an instance of a social norm formed through legal, technical, and cultural dimensions.Institutions such as regulators, industry, and researchers act as societal agents that both influence and respond to evolving norms.Attempts to promote privacy must account for this complexity and the dynamic interactions among these actors.Privacy enhancing technologies (PETs) are technical solutions that allow for the development of solutions that benefit society, while ensuring the privacy of the individuals whose data is being used.However, despite increased privacy challenges and a corresponding increase in new regulations across the globe, a low adoption rate of PETs persists.In this work, we investigate the factors influencing industry's decision-making processes around PETs adoption as well as the extent to which privacy regulations inspire such adoption through a qualitative survey study with 22 industry participants from across Canada Informed by the results of our analysis, we make recommendations for industry, researchers, and policymakers on how to support what each of them seeks from the other when attempting to improve digital privacy protections.By advancing our understanding of what challenges the industry faces, we increase the effectiveness of future privacy research that aims to help overcome these issues.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.042 | 0.015 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".