PrivGuide: A Planning Tool for Proactive Privacy Integration in the DevPrivOps Lifecycle
Bibliographic record
Abstract
Privacy concerns are rapidly growing, demanding tighter integration within the Agile software development lifecycle. Existing approaches rely on audits that are not integrated into the DevOps lifecycle, which greatly hinders both the analysis of privacy risks and the correction of privacy flaws. Even integrated tools are primarily reactive, treating privacy as an afterthought rather than a design principle. The currently used privacy engineering methodologies tend to be too general and have lackluster tooling to be enforced. Furthermore, privacy quantification is very domain-dependent, which makes it hard to automate without the help of an expert. DevPrivOps, a proposed extension to DevOps, focused on continuous privacy verification and user transparency, suffers from a lack of concrete tools. This paper proposes a novel tool for the DevPrivOps lifecycle that bridges the gap between privacy engineering methodology and the Agile planning phase, promoting the adoption of privacy by design strategies. This tool serves as a foundation for building and integrating further DevPrivOps tools, fostering a proactive approach to privacy within Agile development. We present the interface of this tool and its role in the DevPrivOps framework
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".