Once is Never Enough: Foundations for Sound Statistical Inference in Tor\n Network Experimentation
Bibliographic record
Abstract
Tor is a popular low-latency anonymous communication system that focuses on\nusability and performance: a faster network will attract more users, which in\nturn will improve the anonymity of everyone using the system. The standard\npractice for previous research attempting to enhance Tor performance is to draw\nconclusions from the observed results of a single simulation for standard Tor\nand for each research variant. But because the simulations are run in sampled\nTor networks, it is possible that sampling error alone could cause the observed\neffects. Therefore, we call into question the practical meaning of any\nconclusions that are drawn without considering the statistical significance of\nthe reported results.\n In this paper, we build foundations upon which we improve the Tor\nexperimental method. First, we present a new Tor network modeling methodology\nthat produces more representative Tor networks as well as new and improved\nexperimentation tools that run Tor simulations faster and at a larger scale\nthan was previously possible. We showcase these contributions by running\nsimulations with 6,489 relays and 792k simultaneously active users, the largest\nknown Tor network simulations and the first at a network scale of 100%. Second,\nwe present new statistical methodologies through which we: (i) show that\nrunning multiple simulations in independently sampled networks is necessary in\norder to produce informative results; and (ii) show how to use the results from\nmultiple simulations to conduct sound statistical inference. We present a case\nstudy using 420 simulations to demonstrate how to apply our methodologies to a\nconcrete set of Tor experiments and how to analyze the results.\n
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 | 0.040 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".