PIR-Tor: scalable anonymous communication using private information retrieval
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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