WFDefProxy: Real World Implementation and Evaluation of Website Fingerprinting Defenses
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, an onion-routing anonymity network, can be attacked by Website Fingerprinting (WF), which de-anonymizes encrypted web browsing traffic by analyzing its unique sequence characteristics. Although many defenses have been proposed, few have been implemented and tested in the real world; most state-of-the-art defenses were only simulated. Simulations fail to capture the real performance of these defenses as they make simplifying assumptions about the protocol stack and network conditions. To allow WF defenses to be analyzed as real implementations, we create WFDefProxy, the first general platform for WF defense implementation on Tor as pluggable transports. We implement three state-of-the-art WF defenses: FRONT, Tamaraw, and RegulaTor. We evaluate each defense extensively by directly collecting defended datasets under WFDefProxy. Our results show that simulation can be inaccurate in many cases. Specifically, Tamaraw’s time overhead was underestimated by 22% in one setting and overestimated by 24% in another. RegulaTor’s time overhead was underestimated by 30–40%. We find that a major source of simulation inaccuracy is that they cannot incorporate how packets depend on each other. We also find that adverse network conditions (which are ignored in simulation), especially congestion, can affect the evaluated overhead of defenses. These results show that it is important to evaluate defenses as implementations instead of only simulations to avoid errors in evaluation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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