Stratified Modelling and Analysis of Confidentiality Requirements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we present a method for modelling and analyzing confidentiality requirements based on requirements stratification. Stakeholders with varying data usage concerns have confidentiality and privacy requirements, and these stakeholders are often in different jurisdictions, for example, national, provincial and local authorities. In addition, customers, such as patient groups and individual patients, have important confidentiality concerns which should be considered in the requirement engineering process. Our approach provides a method to model and analyze the interactions of the different requirements with their inherent stratified relationship and supports the iterative specification and analysis of the requirements. We report on a preliminary evaluation of the method with a case study in the health care domain. Our results show that our method is suitable to express most case study requirements in their natural stratification order, but it also uncovered important limitations. Nevertheless, our method was effective in detecting a potential incompleteness in the subject requirements set.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it