Reusing knowledge on delivering privacy and transparency together
Bibliographic record
Abstract
Heavy reliance on modern technologies causes the concepts of transparency and privacy to become more and more intertwined. Some recent privacy incidents illustrates that, such as: sharing of personal health information between United States and Canadian border services agencies; enabling voice recognition software by default in Samsung's smart TVs; accidentally collecting personal information such as emails, addresses, user IDs and passwords by Google Street View car. However, all of these incidents lack transparency in disclosing features that could trigger privacy violation; leaving the general public unaware of what, how and when their personal information or information about their behaviour is being collected and used. Developing software that addresses both qualities is a challenge. Capturing patterns of knowledge that represent alternatives to achieve Privacy requirements together with Transparency properties can help software engineers to model more comprehensive solutions. We use Softgoal Interdependencies Graphs (SIG) to capture such patterns. This paper demonstrates a set of softgoal interdependency graphs (SIG) illustrating how transparency and privacy impact each other.
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.
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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".