Bibliographic record
Abstract
• By using efficient algorithms it is possible to explore the data flows enabled by RBAC systems, with various combinations of subjects and roles assigned to subjects. • Given RBAC configurations, it is possible to determine what are the levels of secrecy (or confidentiality) and integrity of the subjects and objects involved. • Different models of access control and data flow control, such as access control matrices, multi-level or label-based access control and RBAC are mutually translatable. • The effects of RBAC role reconfigurations on secrecy and integrity can be evaluated. We show how data security concepts such as data flow, secrecy (or confidentiality) and integrity can be defined for RBAC, Role-Based Access Control. In contrast to the prevailing literature that uses a lattice model to express such concepts, we demonstrate the use of a partial order model that is more general. This is done by using the concepts of “partial order of equivalence classes” and of “security labels” that can be associated with RBAC subjects and objects and determine their mutual data flows, as well as their secrecy and integrity properties. Our model allows to reason on RBAC configurations with different assignments of roles to subjects. On the converse, we demonstrate a method for obtaining RBAC configurations from data security requirements or security label assignments. These results are supported by a proof showing that three methods for defining data flow: by access control matrices or lists, by labels and by roles, are equivalent and mutually convertible by efficient algorithms. We show how RBAC state changes, or “reconfigurations” can be defined in this framework, and what are the effects of elementary reconfigurations on data flow, secrecy and integrity of data.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".