An Empirical Evaluation of Relay Selection in Tor.
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
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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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