ExperimenTor: a testbed for safe and realistic tor experimentation
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
Tor is one of the most widely-used privacy enhancing technologies for achieving online anonymity and resisting censorship. Simultaneously, Tor is also an evolving research network in which investigators perform experiments to improve the network’s resilience to attacks and enhance its performance. Existing methods for studying Tor have included analytical modeling, simulations, small-scale network emulations, small-scale PlanetLab deployments, and measurement and analysis of the live Tor network. Despite the growing body of work concerning Tor, there is no widely accepted methodology for conducting Tor research in a manner that preserves realism while protecting live users ’ privacy. In an effort to propose a standard, rigorous experimental framework for conducting Tor research in a way that ensures safety and realism, we present the design of ExperimenTor, a largescale Tor network emulation toolkit and testbed. We report our early experiences with prototype ExperimenTor testbeds deployed at three research institutions. 1
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 | 0.000 | 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