Walkie-Talkie: An Effective and Efficient Defense against Website Fingerprinting
Bibliographic record
Abstract
Website fingerprinting is a traffic analysis attack that allows an eavesdropper to determine the web activity of a client, even if the client is using privacy technologies such as proxies, VPNs, or Tor. Effective defenses against website fingerprinting hamper user experience due to their large bandwidth overhead and time overhead, requiring more than a half minute to load a page on average. In this work we propose a new defense against website fingerprinting, Walkie-Talkie, with a small overhead that can confuse even a perfectly classifying attacker. Walkie-Talkie modifies the browser to communicate in half-duplex mode rather than the usual full-duplex mode, thus restricting the feature set available to the attacker. We then add random padding to further confuse the attacker. With Walkie-Talkie, at a bandwidth overhead of 32% and time overhead of 9%, the perfect attacker’s false positive rate exceeds 5%; at a bandwidth overhead of 55%, the perfect attacker’s false positive rate exceeds 10%. Our defense therefore allows webbrowsing clients to defend their privacy against website fingerprinting both effectively and efficiently.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".