An Innovative Key Management Model for Broadcasting to Remote Cooperative Groups
Bibliographic record
Abstract
In Mobile Ad Hoc Networks(MANETs), a set of interacting nodes should cooperatively implement the routing functions to enable end-to-end communication along dynamic paths composed by multi-hop wireless links. MANETs have been proposed to serve as an effective networking system facilitating information exchange between mobile devices even without fixed infrastructures. In MANETs, it is important to support group-oriented applications, such as audio/video conference and one-to-many data dissemination in disaster or battlefield rescue scenarios. In the above group oriented communication scenarios, the common problem is to enable a sender to securely transmit secret messages to a remote cooperative group. A solution to the above problem must meet several constraints. First, the sender must be remote and can be dynamic. Second, the message transmission may cross various networks including open insecure networks before reaching the intended recipients. Third, the data communication from the group members to the sender may be limited. Also, the sender may wish to choose only a subset of the overall group as the intended recipients. Furthermore, it is hard to resort to a fully trusted third party to secure the overall communication. In contrast to the above constraints, mitigating features are that the group members are cooperative and the secret communication among them is local and efficient. This paper exploits these mitigating features to facilitate the remote access control of group-oriented communications without relying on a fully trusted secret key generation center.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".