Enhancing the Unlinkability of Circuit-Based Anonymous Communications with k-Funnels
Bibliographic record
Abstract
Anonymous communication systems are essential tools for preserving privacy and freedom of expression. However, traffic analysis attacks make it challenging to maintain unlinkability in circuit-based anonymity networks like Tor, enabling adversaries to deanonymize communications. To address this problem, we introduce k-funnel, a new security primitive that enhances the unlinkability of circuit-based anonymity networks, and we present BriK, a Tor pluggable transport that implements k-funnels. k-Funnels offer k-anonymity to a group of k clients by jointly tunneling their circuits' traffic through a bridge while ensuring that the client-generated flows are indistinguishable. BriK incorporates several defense mechanisms against traffic analysis attacks, including traffic shaping schemes, synchronization protocols, and approaches for monitoring exposure to statistical disclosure attacks. Our evaluation shows that BriK is able to support web browsing and video streaming while offering k-anonymity. We evaluate the security of BriK against traffic correlation attacks leveraging state-of-the-art deep learning classifiers without considering auxiliary information and find it highly resistant. Although k-funnels require the cooperation of mutually trusted clients, limiting their coordination, our work presents a new practical solution to strengthen unlinkability in circuit-based anonymity systems.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".