Walkie-talkie: an efficient defense against passive website fingerprinting attacks
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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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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".