Bibliographic record
Abstract
Role-based Access Control (RBAC) is a popular solution for implementing information security however there is no pervasive methodology used to produce scalable access control systems for large organizations with hundreds or thousands of employees. As a result ten engineers will likely arrive at ten different solutions to the same problem where there is no right or wrong answer but there is both an immediate and long term cost. Moreover, they would have difficulty communicating the important aspects of their design implementations to each other. This is an interesting deficiency because despite their diversity, large organizations are built upon two key concepts, roles and responsibilities, where a role like Departmental Chair is identified and assigned responsibilities. In this paper, our objective is to introduce ORGODEX, a new model and practical methodology for engineering scalable RBAC systems in large organizations where employees require access to information on a need to know basis. First, we motivate the requirement for a new RBAC dichotomy, distinguishing between roles and responsibilities. Next, we introduce our new model for describing and reasoning about RBAC systems with this new dichotomy. Finally, we produce a new iterative methodology for engineering scalable access control systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.770 | 0.608 |
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".