Relationship-based access control policies and their policy languages
Bibliographic record
Abstract
The Relationship-Based Access Control (ReBAC) model was recently proposed as a general-purpose access control model. It supports the natural expression of parameterized roles, the composition of policies, and the delegation of trust. Fong proposed a policy language that is based on Modal Logic for expressing and composing ReBAC policies. A natural question is whether such a language is representationally complete, that is, whether the language is capable of expressing all ReBAC policies that one is interested in expressing. In this work, we argue that the extensive use of what we call Relational Policies is what distinguishes ReBAC from traditional access control models. We show that Fong’s policy language is representationally incomplete in that certain previously studied Relational Policies are not expressible in the language. We introduce two extensions to the policy language of Fong, and prove that the extended policy language is representationally complete with respect to a well-defined subclass of Relational 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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".