P2U: A Privacy Policy Specification Language for Secondary Data Sharing and Usage
Bibliographic record
Abstract
Within the last decade, there are growing economic social incentives and opportunities for secondary use of data in many sectors, and strong market forces currently drive the active development of systems that aggregate user data gathered by many sources. This secondary use of data poses privacy threats due to unwanted use of data for the wrong purposes such as discriminating the user for employment, loan and insurance. Traditional privacy policy languages such as the Platform for Privacy Preferences (P3P) are inadequate since they were designed long before many of these technologies were invented and basically focus on enabling user-awareness and control during primary data collection (e.g. by a website). However, with the advent of Web 2.0 and Social Networking Sites, the landscape of privacy is shifting from limiting collection of data by websites to ensuring ethical use of the data after initial collection. To meet the current challenges of privacy protection in secondary context, we propose a privacy policy language, Purpose-to-Use (P2U), aimed at enforcing privacy while enabling secondary user information sharing across applications, devices, and services on the Web.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".