Trust Trade-off Analysis for Security Requirements Engineering
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
Security requirements often have implicit assumptions about trust relationships among actors. The more actors trust each other, the less stringent the security requirements are likely to be. Trust always involves the risk of mistrust; hence, trust implies a trade-off: gaining some benefits from depending on a second party in trade for getting exposed to security and privacy risks. When trust assumptions are implicit, these trust trade-offs are made implicitly and in an ad-hoc way. By taking advantage of agent- and goal-oriented analysis, we propose a method for discovering trade-offs that trust relationships bring. This method aims to help the analyst select among alternative dependency relationships by making explicit trust trade-offs. We propose a simple algorithm for making the trade-offs in a way that reaches a balance between costs and benefits.
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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