Telex: anticensorship in the network infrastructure
Bibliographic record
Abstract
In this paper, we present Telex, a new approach to resisting state-level Internet censorship. Rather than attempting to win the cat-and-mouse game of finding open proxies, we leverage censors ’ unwillingness to completely block day-to-day Internet access. In effect, Telex converts innocuous, unblocked websites into proxies, without their explicit collaboration. We envision that friendly ISPs would deploy Telex stations on paths between censors’ networks and popular, uncensored Internet destinations. Telex stations would monitor seemingly innocuous flows for a special “tag ” and transparently divert them to a forbidden website or service instead. We propose a new cryptographic scheme based on elliptic curves for tagging TLS handshakes such that the tag is visible to a Telex station but not to a censor. In addition, we use our tagging scheme to build a protocol that allows clients to connect to Telex stations while resisting both passive and active attacks. We also present a proof-of-concept implementation that demonstrates the feasibility of our system. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".