Reusable Knowledge for Achieving Privacy: A Canadian Health Information Technologies Perspective.
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
Privacy is a fundamental aspect when dealing with Personal Information. Privacy requirements are those that capture privacy goals and its associated measures for a system under development. In order to ensure privacy we must identify these elements. However, there are many challenges in their identification. For example, privacy requirements may be difficult to quantify and precisely specify. There is a need for systematic approaches for reasoning, modeling and analyzing privacy from the early stages of the software development. Furthermore, it is necessary to develop a usable ontology or classification of measurable aspects of privacy that can be used to aid in the specification of privacy requirements. These ontologies should be represented in a way that facilitates their use as guidelines for the requirements elicitation process. This work builds on a review of privacy legislation to develop a catalog of aspects of privacy that can be considered during requirements gathering. This catalogue is used to guide the requirements engineer through alternatives for achieving privacy. The approach uses the i * framework to model privacy as a special type of goal. We show how privacy can be modelled through different viewpoints with different alternatives for its operationalization. An example in the health care domain is used to illustrate our work. 1.
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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 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