Preserving Opacity on Interval Markov Chains under Simulation
Bibliographic record
Abstract
Given a probabilistic transition system (PTS) A partially observed by an attacker, and an ¿¿-regular predicate ¿¿ over the traces of A, measuring the disclosure of the secret ¿¿ in A means computing the probability that an attacker who observes a run of A can ascertain that its trace belongs to ¿¿. We consider specifications given as Interval Markov Chains (IMCs), which are underspecified Markov chains where probabilities on edges are only required to belong to intervals. Scheduling an IMC S produces a concrete implementation as a PTS and we define the worst case disclosure of secret ¿¿ in S as the maximal disclosure of ¿¿ over all PTSs thus produced. We compute this value for a subclass of IMCs and we prove that simulation between specifications can only improve the opacity of implementations.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".