A Privilege Creep-Aware Role Mining Method for Enhanced Access Control Security
Bibliographic record
Abstract
Role Mining (RM) extracts Role-Based Access Control (RBAC) structures from user-permission assignments to reduce administrative overhead. However, existing approaches usually make the assumption of clean datasets, while real-world systems suffer from anomalies like privilege creep, the gradual accumulation of unnecessary permissions.The proposed approach aims to detect potential privilege crept users who should be reviewed first, and identify legitimate permissions assignments to be expressed in RBAC, reducing management complexity. It consists of a two-step procedure: clean the User-Permission Assignment matrix (UPA) using a clustering and statistical analysis, then build an RBAC state using a regular role mining algorithm.The proposed approach yields an average of 90% in privilege creep detection accuracy and over 95% privilege creep correction, evaluated on synthetically made datasets. Evaluation on real-world datasets demonstrates an average 4-fold reduction in required roles while maintaining at least 80% UPA coverage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".