Dynamic distributed trust model to control access to resources over the Internet
Bibliographic record
Abstract
Traditional security tools and infrastructures have proven to be inadequate, inflexible, and difficult to apply in the incredibly large Internet of today. Existing security systems deal mainly with authentication and access control and are not suitable for the increasingly demanding trust requirements in today's network-based applications. In this paper, a general-purpose, application-independent dynamic distributed trust model (DDTM) that is suitable for access control in the Internet applications is proposed. The core of this model is the recommendation trust model organized as a trust delegation tree and authorization delegation realized by a delegation certificate. DDTM provides a distributed key-oriented certificate issuing mechanism with no centralized global authority. The service authorities can create their own trust policy and control access to the services owned by them. In this paper, we first point out the insufficiency of the existing access control mechanisms and review several method for expressing trust. We then propose the dynamic distributed trust model that works over the Internet. Finally, we focus on the detail operations of trust delegation tree.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".