A Delphi study on the role of privacy enhancing technologies (PETs) in data sharing ecosystems
Bibliographic record
Abstract
Privacy-enhancing technologies (PETs) have the potential to revolutionize data sharing by streamlining time-consuming and complex risk assessment processes without sacrificing privacy and increasing risks. To realize the potential of PETs, the paper proposes that the use of PETs needs to be better communicated, promoted and legitimized. This paper provides a consensus (Delphi) study with a global panel of experts on PETs who convened the United Nations in the context of data innovation for the global community of official statistics. This panel evaluated statements and recommendations of the use of PETs in the risk assessment process for data sharing. The panel agreed that the use of PETs can improve use of the Five Safes framework for data sharing agreements. While best practices for PET deployment are still being established, the potential benefits of PETs should be communicated more effectively by emphasizing the importance of objectivity regarding the benefits and limitations rather than over-promising the benefits (30). The panel recommended institutional assessment of the utility trade-off in the use of PETs and buy-in beyond the technology domain. It was also recommended that regulators should play an active role in guiding how organizations can combine core data protection principles with PETs to supplement other measures. The panel further recommended developing standardized, PETs-specific terminology to facilitate education and communication about PETs with key stakeholders.
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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 | 0.103 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".