PIR-Tor: scalable anonymous communication using private information retrieval
Bibliographic record
Abstract
Existing anonymous communication systems like Tor do not scale well as they require all users to maintain up-todate information about all available Tor relays in the system. Current proposals for scaling anonymous communication advocate a peer-to-peer (P2P) approach. While the P2P paradigm scales to millions of nodes, it provides new opportunities to compromise anonymity. In this paper, we step away from the P2P paradigm and advocate a client-server approach to scalable anonymity. We propose PIR-Tor, an architecture for the Tor network in which users obtain information about only a few onion routers using private information retrieval techniques. Obtaining information about only a few onion routers is the key to the scalability of our approach, while the use of private retrieval information techniques helps preserve client anonymity. The security of our architecture depends on the security of PIR schemes which are well understood and relatively easy to analyze, as opposed to peer-to-peer designs that require analyzing extremely complex and dynamic systems. In particular, we demonstrate that reasonable parameters of our architecture provide equivalent security to that of the Tor network. Moreover, our experimental results show that the overhead of PIR-Tor is manageable even when the Tor network scales by two orders of magnitude. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".