A Delphi study on the role of privacy enhancing technologies (PETs) in data sharing ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it