Secret-key agreement over unauthenticated public channels-part II: the simulatability condition
Bibliographic record
Abstract
For pt.I see ibid., vol.49, no.4, p.822-31(2003). In the first part, we showed that when two parties, willing to generate a secret key, but connected only by a completely insecure communication channel, have access to independent repetitions of some random experiment, then the possibility of secret-key agreement depends on a certain property, called simulatability, of the probability distribution modeling the parties' initial knowledge. More generally, the simulatability condition is important in the context of identification and authentication among parties sharing some correlated but not necessarily identical partially secret keys. Unfortunately, this condition is a priori not very useful since it is not clear how to decide efficiently whether it is satisfied or not for a given distribution P/sub XYZ/. We introduce a new formalism, based on a mechanical model for representing the involved quantities, that allows for dealing with discrete joint distributions of random variables and their manipulations by noisy channels. We show that this representation leads to a simple and efficient characterization of the possibility of secret-key agreement secure against active adversaries.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".