Improving personal privacy in social systems with people-tagging
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.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".