A secured hierarchical trust management framework for public computing utilities
Bibliographic record
Abstract
This paper presents a hierarchical (two-layered) trust management framework for very large scale distributed computing utilities where public resources provide majority of the resource capacity. The dynamic nature of these utility networks introduce challenging management and security issues due to behavior turnabout, maliciousness and diverse policy enforcement. The trust management approach offers interesting answers to such issues. In our framework, the lower layer computes local reputation for peers within their domain based on individual contribution, while the upper layer combines the local reputation with that of its domain's (as perceived by other domains) to compute the peer's global trust. Simulation results show that the hierarchical scheme is more scalable, highly robust in hostile conditions and capable of creating rapid trust estimates. Features of the framework include: (a) ability to carry forward local behavior trends, (b) autonomous domain-based policing, (c) high cohesiveness with the resource management system, and (d) securely exposing the trust evaluation operations to peers (i.e., the subjects of the evaluation process). A detailed analysis of the threats/attacks that the framework could be subjected is presented along with countermeasures against the attacks.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".