Designing for privacy and other competing requirements
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
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.
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 | 0.000 | 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 | 0.000 | 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