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
Protecting metadata of communications has been an area of active research since the dining cryptographers problem was introduced by David Chaum in 1988. The Snowden revelations from 2013 resparked research in this direction. Consequently over the last decade we have witnessed a flurry of novel systems designed to protect metadata of users' communications online. However, such systems leverage different assumptions and design choices to achieve their goal; resulting in a scattered view of the desirable properties, potential vulnerabilities, and limitations of existing metadata-protecting communication systems (MPCS). In this work we survey 31 systems targeting metadata-protected communications, and present a unified view of the current state of affairs. We provide two different taxonomies for existing MPCS, first into four different categories by the precise type of metadata protections they offer, and next into six families based on the core techniques that underlie them. By contrasting these systems we identify potential vulnerabilities, as well as subtle privacy implications of design choices of existing MPCS. Furthermore, we identify promising avenues for future research for MPCS, and desirable properties that merit more attention.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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