Walkie-talkie: an efficient defense against passive website fingerprinting attacks
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
Website fingerprinting (WF) 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. Recent work has highlighted the threat of website fingerprinting to privacy-sensitive web users. Many previously designed defenses against website fingerprinting have been broken by newer attacks that use better classifiers. The remaining effective defenses are inefficient: they hamper user experience and burden the server with large overheads. In this work we propose Walkie-Talkie, an effective and efficient WF defense. Walkie-Talkie modifies the browser to communicate in half-duplex mode rather than the usual full-duplex mode; half-duplex mode produces easily moldable burst sequences to leak less information to the adversary, at little additional overhead. Designed for the open-world scenario, Walkie-Talkie molds burst sequences so that sensitive and non-sensitive pages look the same. Experimentally, we show that Walkie-Talkie can defeat all known WF attacks with a bandwidth overhead of 31% and a time overhead of 34%, which is far more efficient than all effective WF defenses (often exceeding 100% for both types of overhead). In fact, we show that Walkie-Talkie cannot be defeated by any website fingerprinting attack, even hypothetical advanced attacks that use site link information, page visit rates, and intercell timing. © 2017 by The USENIX Association. All Rights Reserved.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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