ConsenSGX: Scaling Anonymous Communications Networks with Trusted Execution Environments
Bibliographic record
Abstract
Abstract Anonymous communications networks enable individuals to maintain their privacy online. The most popular such network is Tor, with about two million daily users; however, Tor is reaching limits of its scalability. One of the main scalability bottlenecks of Tor and similar network designs originates from the requirement of distributing a global view of the servers in the network to all network clients. This requirement is in place to avoidepistemic attacks, in which adversaries who know which parts of the network certain clients do and do not know about can rule in or out those clients from being responsible for particular network traffic. In this work, we introduce a novel solution to this scalability problem by leveraging oblivious RAM constructions and trusted execution environments in order to enable clients to fetch only the parts of the network view they require, without the directory servers learning which parts are being fetched. We compare the performance of our design with the current Tor mechanism and other related works to show one to two orders of magnitude better performance from an end-to-end perspective. We analyse the requirements to actually deploy such a scheme today and conclude that it would only require a small fraction (<2.5%) of the relays to have the required hardware support; moreover, these relays can perform their roles with minimal network bandwidth requirements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".