Scalability issues in pmi delegation
Bibliographic record
Abstract
The Canadian Department of National Defence (DND) is shifting its methods for the delegation and exercise of authority from paper-based to electronic-based means. DND has deployed a commercial PKI but there is no general technical solution presently employed by DND for access control or electronic authorization of workflow in distributed processing environments. The aim of this research is to show how an authorization system, or privilege management infrastructure (PMI), can be used to support business processes DND. The results are expected to be applicable to large enterprises in general. The research demonstrates how ITU-T standard X.509 can be used to support DND authority and delegation models. The investigation involves the analysis of the key authorizations within a specific DND problem domain. The X.509 standard and concepts from role-based access control form the basis of the PMI design. This involves the use of attribute certificates to control the specification and delegation of privileges. A novel interpretation of X.509 attribute certificates is proposed that provides separate hierarchies of responsibility for the management and delegation of roles. The results provide insight into, and quantification of, the complexity of the resulting delegation chains. The use of a roles based model for delegation is seen as being important to the scaling of PMI to service large enterprises with mature, complex authority structures. If the processing complexity can be managed, the flexibility of being able to model the actual privilege delegation paths in an organization is an advantage of a rolebased model. 1.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".