A white-box policy analysis and its efficient implementation
Bibliographic record
Abstract
In policy composition frameworks, such as XACML, composite policies can be formed by the application of policy composition algorithms (PCAs), which combine authorization decisions of component policies. Understanding the behaviour of composite policies is a non-trivial endeavour, but instrumental in the engineering of correct access control policies. Existing policy analyses take a black-box approach, in which the global behaviour of the composite policy is assessed. A black-box approach is useful for detecting the presence of erroneous behaviour, but not particularly useful for locating the source of the error. In this work, we propose a white-box policy analysis, known as Decision in Context (DIC), that assesses the behaviour of component policies situated in a composite policy. We show that the DIC query can be applied to facilitate policy change impact analysis, break-glass reduction analysis, dead policy identification, as well as the pruning of redundant subpolicies. For generality, the DIC query is defined in an XACML-style policy composition framework that is agnostic of the underlying access control model. The DIC query is implemented via a reduction to either propositional satisfiability (SAT) or pseudo boolean satisfiability (PBS) instances, after which standard solvers can be invoked to complete the evaluation. Empirical analyses have been conducted to compare the relative efficiency of the SAT and PBS encodings. The latter is found to be a more effective encoding, especially for composite policies containing majority-voting PCAs.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".