Trust modeling and its applications for peer-to-peer based systems
Bibliographic record
Abstract
Organizing large-scale network computing systems in a peer-to-peer (P2P) fashion is a manifestation of one of the fundamental design principles on the Internet. Current research is focusing on improving P2P systems and one of the future directions is to combine P2P and Grid technologies. One of the key issues identified in the evolution of P2P technologies is the trust issue. This thesis presents a trust model for P2P structured large-scale network computing systems. The most widely used trust modeling approach is to use a network of recommenders to obtain references and use these to predict the trust between two entities. This approach is known to suffer from drawbacks such as trustworthiness of the recommenders and scalability. To address this problem, a solution is proposed where a recommender is independently evaluated using accuracy and honesty measures. This thesis explains using simulation results how the separation of accuracy and honesty helps in addressing the above issues. To demonstrate the utility of the trust model, a trust aware resource allocation model is developed such that it can be used to make trust cognizant resource allocations. To the best of our knowledge, this is the first study to integrate trust into resource management systems. The simulation results indicate that significant preferences gain can be obtained through this integration.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".