Verification of Access Control Policies for REA Business Processes
Bibliographic record
Abstract
Access control is a significant aspect of security and constitutes an important component of operating systems, database management systems (DBMS), and applications. Access control policies define which users have access to what objects and operations and describe any existing constraints. These policies are not only different from one organization to another but also change over time, even in a single organization. We examine the integration, not necessarily the inclusion, of these policies into business processes and consider such effects as consistency. Determining the effects of these policies can become difficult because several such policies exist, and taking into account all possible combinations or executions of these policies is tedious and error-prone. In addition, the number of policies usually increases over time and adds to the complexity of analyzing their combinations. It is acknowledged in the literature that what you specify is what you get, but that is not necessarily what you want. To show our approach, we specify certain access control policies for Resource--Event--Agent (REA) business processes and examine the addition and combination of these policies. More specifically, we illustrate the principal of separation of duties (e.g., two separate individuals must authorize ordering items and paying for them). Our main contribution is the verification of access control policies in conjunction with a REA business process.
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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 | 0.032 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".