Bibliographic record
Abstract
Abstract Access control is an information security process which guards protected resources against unauthorized access, as specified by restrictions in security policies. A variety of policy languages have been designed to specify security policies of systems. In this paper, we introduce a certified policy language, called TEpla, with formal semantics and simple language constructs, which we have leveraged to express and formally verify properties about complex security goals. In developing TEpla, we focus on security in operating systems and exploit security contexts used in the Type Enforcement mechanism of the SELinux security module. TEpla is certified in the sense that we have encoded the formal semantics and machine-checked the proofs of its properties using the Coq Proof Assistant. In order to express the desired properties, we first analyze the behavior of the language by defining different ordering relations on policies, queries, and decisions. These ordering relations enable us to evaluate how algorithms for deciding whether or not requests are granted by policies will react to changes in policies and queries. The machine-checked mathematical proofs guarantee that TEpla behaves as prescribed by the semantics. TEpla is a crucial step toward developing certifiably correct policy-related tools for Type Enforcement policies.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".