Bibliographic record
Abstract
ABSTRACT: The specification and validation of security protocols often requires viewing function calls - like encryption/decryption and the generation of fake messages explicitly as actions within the process semantics. Following this approach, this paper introduces a symbolic framework based on value-passing processes able to handle symbolic values like fresh nonces, fresh keys, fake addresses and fake messages. The main idea in our approach is to assign to each value-passing process a formula describing the symbolic values conveyed by its semantics. In such symbolic processes, called constrained processes, the formulas are drawn from a logic based on a message algebra equipped with encryption, signature and hashing primitives. The symbolic operational semantics of a constrained process is then established through semantic rules updating formulas by adding restrictions over the symbolic values, as required for the process to evolve. We then prove that the logic required from the semantic rules is decidable. We also define a bisimulation equivalence between constrained processes; this amounts to a generalisation of the standard bisimulation equivalence between (non-symbolic) value-passing processes. Finally, we provide a complete symbolic bisimulation method for constructing the bisimulation between constrained processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".