Bibliographic record
Abstract
Tor is a network designed for low-latency anonymous communications. Tor clients form circuits through relays that are listed in a public directory, and then relay their encrypted traffic through these circuits. This indirection makes it difficult for a local adversary to determine with whom a particular Tor user is communicating. In response, some local adversaries restrict access to Tor by blocking each of the publicly listed relays. To deal with such an adversary, Tor uses bridges, which are unlisted relays that can be used as alternative entry points into the Tor network. Unfortunately, issues with Tor's bridge implementation make it easy to discover large numbers of bridges. An adversary that hoards this information may use it to determine when each bridge is online over time. If a bridge operator also browses with Tor on the same machine, this information may be sufficient to deanonymize him. We present BridgeSPA as a method to mitigate this issue. A client using BridgeSPA relies on innocuous single packet authorization (SPA) to present a time-limited key to a bridge. Before this authorization takes place, the bridge will not reveal whether it is online. We have implemented BridgeSPA as a working proof-of-concept, which is available under an open-source licence.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.021 |
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".