Specifying Personal Privacy Policies to Avoid Unexpected Outcomes.
Bibliographic record
Abstract
The growth of the Internet is increasing the deployment of e-services in such areas as e-commerce, e-learning, and e-health. In parallel, the providers and consumers of such services are realizing the need for privacy. The use of P3P privacy policies on web sites is an example of this growing concern for privacy. Managing privacy using privacy policies is a promising approach. In this approach, an e-service provider and an e-service consumer each have separate privacy policies. Before an e-service is engaged, the provider's policy must be "compatible" with the consumer's policy. However, beyond compatibility, the policies may lead to unexpected outcomes. This can result in the lost of privacy and even lead to serious injury in certain cases. This paper gives examples of how such outcomes can arise and suggests how the consumer's personal privacy policy can be modified to avoid such outcomes.
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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 | 0.058 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".