Engineering Privacy for Big Data Apps with the Unified Modeling Language
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
This paper describes proposed privacy extensions to UML to help software engineers to quickly visualize privacy requirements, and design privacy into big data applications. To adhere to legal requirements and/or best practices, big data applications will need to apply Privacy by Design principles and use privacy services, such as, and not limited to, anonymization, pseudonymization, security, notice on usage, and consent for usage. We extend UML with ribbon icons representing needed big data privacy services. We further illustrate how privacy services can be usefully embedded in use case diagrams using containers. These extensions to UML help software engineers to visually and quickly model privacy requirements in the analysis phase, this phase is the longest in any software development effort. As proof of concept, a prototype based on our privacy extensions to Microsoft Visio's UML is created and the utility of our UML privacy extensions to the Use Case Diagram artifact is illustrated employing an IBM Watson-like commercial use case on big data in a health sector application.
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 | 0.000 |
| 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 | 0.001 | 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