Secret Key Generation within Peer-to-Peer Network Overlays
Bibliographic record
Abstract
Key generation, a well known alternative to key distribution, allows two (or more) parties to concurrently generate the same secret key through their independent measurements of a mutually observable random information source. Within wireless networks the reciprocity of channel characterization measurements can be used to provide this required random source of information. This work enables key generation within peer-to-peer wired network by algorithmically extending the notion of wireless reciprocity into the wired domain. It is shown that for larger-scale Erdos-Renyi style peer-to-peer networks, the developed key generation approach remains secure when up to 75% of the peer-to-peer network's edge are assumed to be adversary controlled. in comparison to prior works, the proposed approach requires zero knowledge of either the network topology or the link capacities allowing it to be particularly well suited to today's global-scale peer-to-peer networks.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".