Designing for privacy and other competing requirements
Bibliographic record
Abstract
Privacy may be interpreted in different ways in different contexts, and may be achieved by means of different mechanisms. It is also frequently intertwined with security concerns. However, other requirements such as functionality, usability and reliability, must also be addressed since they often compete among each other. While the understanding of technical mechanisms for addressing privacy has been growing, systematic approaches are needed to guide software engineers to elicit, model and reason about privacy requirements and to address them during design. In a networked world, multi-agent systems have been emerging as a new approach. Each agent may have his own goals and beliefs and social relationships with each other. Each agent may have his own perspective concerning privacy. Perspectives from different agents may conflict with each other. Moreover, they may conflict with other requirements such as availability and performance. In this paper we present a framework to model the way agents interact with each other to achieve their goals. The framework uses a catalogue to guide the software engineer through alternatives for achieving privacy. Each alternative will be modeled showing how it contributes to privacy as well as to other requirements within this agent or in other agents. The approach is based on the i* framework. Privacy is modeled as a special type of goal. We show how one can model privacy concerns for each agent and the different alternatives for operationalizing it. An example in the health care domain is used to illustrate.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 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".