Algebraic Framework for the Specification and Analysis of Cryptographic-Key Distribution
Bibliographic record
Abstract
Several organizations generate and store a wide range of information in what is commonly referred to as data stores. To access the information within these data stores, two main architectures are widely adopted. The first architecture gives access to information through a trusted server that enforces established confidentiality policies. The second one allows the information to be public but in its encrypted form. Then through a scheme for the distribution of cryptographic keys, each user is provided with the keys needed to decrypt only the part of the information she is authorized to access. This paper relates to the latter architecture. We introduce an algebraic framework that takes into consideration a new perspective in tackling the key-distribution problem. We use the proposed framework to analyze key-distribution schemes that are representative of the ones found in the literature. The framework enables the specification and the verification of key-distribution policies. We also point to several other applications related to measures ensuring information confidentiality.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".