Resolving XACML Rule Conflicts using Artificial Intelligence
Bibliographic record
Abstract
The XACML access control policy specification language provides a simple rule/policy combining algorithm that is invoked when a request is evaluated against a particular policy set, and the results of the policy decision point (PDP) include solutions with both "permit" and "deny" effects. In short, the combining algorithm allows the policy writer to specify which effect should prevail in case of such conflicts. This feature has long been considered as misleading, and a wide variety of research has been done in an attempt to extend it using supplementary language features or algorithms based on priority definitions. We propose a new algorithm that, instead of absolute priorities expressed as numbers, is based on relative priorities that do not use numerical scales. Two kinds of annotations need to be added to policies, one that says if the value of an attribute is sensitive and another that provides information that can be used to determine which attribute is most important in the case when several sensitive values are encountered during the processing of attribute values in a request. This information serves as input to our decision making mechanism, designed to respect the user-specified priorities as best as possible.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".