A large-scale empirical study of P3P privacy policies
Bibliographic record
Abstract
Numerous studies over the past ten years have shown that concern for personal privacy is a major impediment to the growth of e-commerce. These concerns are so serious that most if not all consumer watchdog groups have called for some form of privacy protection for Internet users. In response, many nations around the world, including all European Union nations, Canada, Japan, and Australia, have enacted national legislation establishing mandatory safeguards for personal privacy. However, recent evidence indicates that Web sites might not be adhering to the requirements of this legislation. The goal of this study is to examine the posted privacy policies of Web sites, and compare these statements to the legal mandates under which the Web sites operate. We harvested all available P3P (Platform for Privacy Preferences Protocol) documents from the 100,000 most popular Web sites (over 3,000 full policies, and another 3,000 compact policies). This allows us to undertake an automated analysis of adherence to legal mandates on Web sites that most impact the average Internet user. Our findings show that Web sites generally do not even claim to follow all the privacy-protection mandates in their legal jurisdiction (we do not examine actual practice, only posted policies). Furthermore, this general statement appears to be true for every jurisdiction with privacy laws and any significant number of P3P policies, including European Union nations, Canada, Australia, and Web sites in the USA Safe Harbor program.
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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 | 0.013 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".