Eliciting confidentiality requirements in practice
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
Confidentiality, the protection of unauthorized disclosure of information, plays an important role in information security of software systems. Security researchers have developed numerous approaches on how to implement confidentiality, typically based on cryptographic algorithms and tight access control. However, less work has been done on defining systematic methods on how to elicit and define confidentiality requirements in the first place. Moreover, most of these approaches are illustrated with simulated examples that do not capture the richness of real world experience. This paper reports on our experiences eliciting confidentiality requirements in a real world project in the health care area. The method applied originates from the M.Sc. thesis of one of the authors and is still considered work in progress. Still, valuable insight into issues of confidentiality requirements engineering can be gained from this case study and we expect that its publication will become a basis for discussion and the definition of a further research agenda in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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