Privacy-enhanced sharing of personal content on the web
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
Publishing personal content on the web is gaining increased popularity with dramatic growth in social networking websites, and availability of cheap personal domain names and hosting services. Although the Internet enables easy publishing of any content intended to be generally accessible, restricting personal content to a selected group of contacts is more difficult. Social networking websites partially enable users to restrict access to a selected group of users of the same network by explicitly creating a "friends' list." While this limited restriction supports users' privacy on those (few) selected websites, personal websites must still largely be protected manually by sharing passwords or obscure links. Our focus is the general problem of privacy-enabled web content sharing from any user-chosen web server. By leveraging the existing "circle of trust" in popular Instant Messaging (IM) networks, we propose a scheme called IM-based Privacy-Enhanced Content Sharing (IMPECS) for personal web content sharing. IMPECS enables a publishing user's personal data to be accessible only to her IM contacts. A user can put her personal web page on any web server she wants (vs. being restricted to a specific social networking website), and maintain privacy of her content without requiring site-specific passwords. Our prototype of IMPECS required only minor modifications to an IM server, and PHP scripts on a web server. The general idea behind IMPECS extends beyond IM and IM circles of trust; any equivalent scheme, (ideally) containing pre-arranged groups, could similarly be leveraged.
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 | 0.000 | 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) | 0.001 | 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