Non-interference in Partial Order Models
Bibliographic record
Abstract
Non-interference (NI) is a property of systems stating that confidential actions should not cause effects observable by unauthorized users. Several variants of NI have been studied for many types of models but rarely for true concurrency or unbounded models. This work investigates NI for High-level Message Sequence Charts (HMSCs), a scenario language for the description of distributed systems, based on composition of partial orders. We first propose a general definition of security properties in terms of equivalence among observations of behaviors. Observations are naturally captured by partial order automata, a formalism that generalizes HMSCs and permits assembling partial orders. We show that equivalence or inclusion properties for HMSCs (and hence for partial order automata) are undecidable, which means in particular that NI is undecidable for HMSCs. We hence consider decidable subclasses of partial order automata and HMSCs. Finally, we define weaker local properties, describing situations where a system is attacked by a single agent, and show that local NI is decidable. We then refine local NI to a finer notion of causal NI that emphasizes causal dependencies between confidential actions and observations and extend it to causal NI with (selective) declassification of confidential events. Checking whether a system satisfies local and causal NI and their declassified variants are PSPACE-complete problems.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".