SGAC: A Multi-Layered Access Control Model with Conflict Resolution Strategy
Bibliographic record
Abstract
Abstract This paper presents SGAC (Solution de Gestion Automatisée du Consentement / automated consent management solution), a new healthcare access control model and its support tool, which manages patient wishes regarding access to their electronic health records (EHR). This paper also presents the verification of access control policies for SGAC using two first-order-logic model checkers based on distinct technologies, Alloy and ProB. The development of SGAC has been achieved within the scope of a project with the University of Sherbrooke Hospital (CHUS), and thus has been adapted to take into account regional laws and regulations applicable in Québec and Canada, as they set bounds to patient wishes: for safety reasons, under strictly defined contexts, patient consent can be overriden to protect his/her life (break-the-glass rules). Since patient wishes and those regulations can be in conflict, SGAC provides a mechanism to address this problem based on priority, specificity and modality. In order to protect patient privacy while ensuring effective caregiving in safety-critical situations, we check four types of properties: accessibility, availability, contextuality and rule effectivity. We conducted performance tests comparison: implementation of SGAC versus an implementation of another access control model, XACML, and property verification with Alloy versus ProB. The performance results show that SGAC performs better than XACML and that ProB outperforms Alloy by two order of magnitude thanks to its programmable approach to constraint solving.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".