Utilizing Semantic Knowledge for Access Control in Pervasive and Ubiquitous Systems
Bibliographic record
Abstract
Controlling access in pervasive environments is crucial and a significant challenge because users and devices can connect from anywhere which results in users and resources becoming available at any point of time and location depending on the situation. Access control policies for this type of environment are required to conform to high-level business notions. In pervasive environments, these high-level notions refer to contexts of the situation which can change unpredictably and must be interpreted semantically to maintain proper access control. Therefore, it is necessary to have a formal representation that represents semantics of the contexts, reflects the change of the situation, and can be shared and understood by a policy system. This paper addresses these issues by introducing a context management system that uses a semantic web approach as an underlying mechanism to model and represent semantics of the contexts. The system stores current contexts in a semantic knowledge base which is used by a semantic access control system in order to form access control policies and evaluate policies at run time. The approach is validated through a proof of concept implementation that includes performance results of the context management system as it responds to a change of the situation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".