Legislative bases for personal privacy policy specification
Bibliographic record
Abstract
The growth of the Internet has been accompanied by a proliferation of e-services, especially in the area of e-commerce (e.g. Amazon.com, eBay.com). However, consumers of these e-services are becoming more and more sensitive to the fact that they are giving up private information every time they use them. At the same time, legislative bodies in many jurisdictions have enacted legislation to protect the privacy of individuals when they need to interact with organizations. As a result, e-services can only be successful if there is adequate protection for user privacy. The use of personal privacy policies to express an individual's privacy preferences appears best-suited to manage privacy for e-commerce. We first motivate the reader with our E-Service Privacy Policy Model that explains how personal privacy policies can be used for e-services. We then derive the minimum content of a personal privacy policy by examining some key privacy legislation selected from Canada, the European Union, and the United States.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".