Semiautomatic Derivation and Use of Personal Privacy Policies in E-Business
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
The growth of the Internet has been accompanied by the growth of Internet e-business services (e.g., electronic bookseller services, electronic stock-transaction services). This proliferation of e-business services has in turn fueled the need to protect the personal privacy of e-business users or consumers. We advocate a privacy policy approach to protecting personal privacy. However, it is evident that the specification of a personal privacy policy must be as easy as possible for the consumer. In this paper, we define the content of personal privacy policies using privacy principles that have been enacted into legislation. We then present two semiautomated approaches for the derivation of personal privacy policies. The first approach makes use of common privacy rules obtained through community consensus. This consensus can be obtained from research and/or surveys. The second approach makes use of existing privacy policies in a peer-to-peer community. We conclude the paper by explaining how personal privacy policies can be applied in e-business to protect consumer privacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it