Lox: Protecting the Social Graph in Bridge Distribution
Bibliographic record
Abstract
In regions of the world where censorship of the Internet is used to limit access to information, monitor the activity of Internet users, and quash dissent, anti-censorship proxies, or bridges, can offer a connection to the open Internet beyond a censor's area of influence. Bridge distribution systems, built to publicly distribute large pools of bridges to users in censored regions, face the inherent conflict of providing bridges to unknown users when some of them may be malicious. If not designed with care, bridge distribution systems can be quickly overwhelmed by attacks from censors, undermining the integrity of the system and the safety of users. It is therefore crucial to prioritize protecting users when developing such systems. In this paper, we present a new bridge distribution system, Lox. Lox prioritizes protecting the privacy of users and their social graphs and incorporates enumeration resistance mechanisms to improve access to bridges and limit the malicious behaviour of censors. We use an updated unlinkable multi-show anonymous credential scheme, suitable for a single credential issuer and verifier, to protect Lox bridge users and their social networks from being identified by malicious actors. We formalize a trust level scheme that is compatible with anonymous credentials and effectively limits malicious behaviour while maintaining user anonymity. Our work includes an open-sourced, Rust implementation of our Lox protocols as well as an evaluation of their performance. With reasonable performance and latency for the expected user base of our system, we demonstrate Lox as a practical, social graph protective bridge distribution system.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".