Abstract Reusable Knowledge for Achieving Privacy: A Canadian Health Information Technologies Perspective
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.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".