An Empirical Evaluation of Relay Selection in Tor.
Bibliographic record
Abstract
While Tor is the most popular low-latency anonymity network in use today, Tor suffers from a variety of performance problems that continue to inhibit its wide scale adoption. One reason why Tor is slow is due to the manner in which clients select Tor relays. There have been a number of recent proposals for modifying Tor’s relay selection algorithm, often to achieve improved bandwidth, latency, and/or anonymity. This paper explores the anonymity and performance trade-offs of the proposed relay selection techniques using highly accurate topological models that capture the actual Tor network’s autonomous system (AS) boundaries, points-of-presence, inter-relay latencies, and relay performance characteristics. Using realistic network models, we conduct a wholenetwork evaluation with varying traffic workloads to understand the potential performance benefits of a comprehensive set of relay selection proposals from the Tor literature. We also quantify the anonymity properties of each approach using our network model in combination with simulations fueled by data from the live Tor network. 1.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".