Optimal Information Release for Mixed Opacity in Discrete-Event Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Opacity is a property of a system that captures whether certain event sequences (or certain states) are indistinguishable from other event sequences (or states) in the system. Opacity is used in analyzing privacy, secrecy, and other aspects of systems modeled by discrete-event systems. In this paper, we introduce the concept of minimal information release policies for non-opacity and the concept of mixed opacity. Mixed opacity policies are introduced as a holistic approach for solving problems that involve a combination of releasing information to make some objectives of the system opaque while making some other objectives non-opaque. We present a set of algorithms for information release under a mixed opacity policy. These algorithms compute policies in a system such that two given sublanguages are opaque, and at the same time, two other sublanguages in the same system are non-opaque. The application of mixed opacity is demonstrated on the Dining Cryptographers Problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it