ConsenSGX: Scaling Anonymous Communications Networks with Trusted Execution Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 avoid epistemic 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it