On Dynamic & Subjective Trust for Privacy Policy Enforcement in Cloud Computing
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
Trust relationships are subjective and dynamic. One entity's trust over a subject is completely independent of some other entity's trust: trust is subjective. Trust also evolves dynamically over time. The growth of cloud computing over the last few years has created an environment where clients need to transact with cloud service providers even though the cloud service clients may or may not completely trust the provider at any given time. There is a need for the provider to acknowledge this dynamic and subjective trust assessment by each client by providing and modifying services individually for each client based on the trust assessment. This paper proposes a policy based approach to the implementation of subjective and dynamic trust so as to enable privacy policy enforcement in a cloud computing environment. We present an abstract model containing computational, storage and monitoring unit with configurable elements and describe algorithms that reflect how a change of trust influences the configuration of these elements. We prototype a system based on the abstract model with benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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