An empirical assessment of approaches to distributed enforcement in role-based access control (RBAC)
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Bibliographic record
Abstract
We consider the distributed access enforcement problem for Role-Based Access Control (RBAC) systems. Such enforcement has become important with RBAC's increasing adoption, and the proliferation of data that needs to be protected. We assess six approaches, each of which has either been proposed in the literature, or is a natural candidate for access enforcement. The approaches are: directed graph, access matrix, authorization recycling, cpol, Bloom filter and cascade Bloom filter. We consider encodings of RBAC sessions in each, and propose and justify a benchmark for the assessment. We present our results from an empirical assessment of time, space and administrative efficiency based on the benchmark. We conclude with inferences we can make regarding the best approach to access enforcement for particular RBAC deployments based on our assessment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it