Analysis and Verification of XACML Policies in a Medical Cloud Environment
Bibliographic record
Abstract
The connectivity of devices, machines and people via Cloud infrastructure can support collaborations among doctors and specialists from different medical organisations. Such collaborations may lead to data sharing and joint tasks and activities. Hence, the collaborating organisations are responsible for managing and protecting data they share. Therefore, they should define a set of access control policies regulating the exchange of data they own. However, existing Cloud services do not offer tools to analyse these policies. In this paper, we propose a Cloud Policy Verification Service (CPVS) for the analysis and the verification of access control policies specified using XACML. The analysis process detects anomalies at two policy levels: a) intra-policy: detects discrepancies between rules within a single security policy (conflicting rules and redundancies), and b) inter-policies: detects anomalies between several security policies such as inconsistency and similarity. The verification process consists in verifying the completeness property which guarantees that each access request is either accepted or denied by the access control policy. In order to demonstrate the efficiency of our method, we also provide the time and space complexities. Finally, we present the implementation of our method and demonstrate how efficiently our approach can detect policy anomalies.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".