Effective attacks and provable defenses for website fingerprinting
Bibliographic record
Abstract
Website fingerprinting attacks allow a local, passive eavesdropper to identify a user’s web activity by lever-aging packet sequence information. These attacks break the privacy expected by users of privacy technologies, including low-latency anonymity networks such as Tor. In this paper, we show a new attack that achieves sig-nificantly higher accuracy than previous attacks in the same field, further highlighting website fingerprinting as a genuine threat to web privacy. We test our attack under a large open-world experimental setting, where the client can visit pages that the attacker is not aware of. We found that our new attack is much more accurate than previous attempts, especially for an attacker monitoring a set of sites with low base incidence rate. We can correctly de-termine which of 100 monitored web pages a client is visiting (out of a significantly larger universe) at an 85% true positive rate with a false positive rate of 0.6%, com-pared to the best of 83 % true positive rate with a false positive rate of 6 % in previous work. To defend against such attacks, we need provably ef-fective defenses. We show how simulatable, determinis-tic defenses can be provably private, and we show that bandwidth overhead optimality can be achieved for these defenses by using a supersequence over anonymity sets of packet sequences. We design a new defense by ap-proximating this optimal strategy and demonstrate that this new defense is able to defeat any attack at a lower cost on bandwidth than the previous best. 1
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".