A Unified Trust Model for Pervasive Environments – Simulation and Analysis
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
Ubiquitous interaction in a pervasive environment is the main attribute of smart spaces. Pervasive systems are weaving themselves in our daily life, making it possible to collect user information invisibly, in an unobtrusive manner by known and even unknown parties. Huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which unifies different trust factors like context, recommendation, and history to calculate the trust level of each party precisely. Trusted computing enables effective solutions to verify the trustworthiness of computing platforms. In this paper, we elaborate Unified Trust Model (UTM) which calculates entity's trustworthiness based on history, recommendation, context and platform integrity measurement, and formally use these factors in trustworthiness calculation. We evaluate UTM behaviour by simulating in different scenario experiments using a Trust and Reputation Models Simulator for Wireless Sensor Networks. We show that UTM offers responsive behaviour and can be used effectively in the low interaction environments.
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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 | 0.002 |
| 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