Improving personal privacy in social systems with people-tagging
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 recent emergence of social systems has transformed the Web from an information pool to a platform for communication and social interaction. As such, the issue of managing privacy of various types of user-created content in these open environments has become more of a concern. Existing social systems often define privacy either as a private/public dichotomy or in terms of a "network of friends relationship, in which all friends" are created equal and all relationships are reciprocal. We explore instead the idea of tagging people to create ego-centric groups of dynamic, non-reciprocal relationships to improve privacy management in this domain. In this paper, we introduce the principles and motivations behind people-tagging, discuss constraints that make people-tagging safe, trustable, and spam-free, describe a research implementation we have created to experiment with the concept, and provide the results of a preliminary empirical evaluation which shows the strength of the idea and indicates areas for future enhancements.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 it