SAS-GKE: A Secure Authenticated Scalable Group Key Exchange
Bibliographic record
Abstract
Secure group communication is one of the challenging issues of present times. With the advancements of the cloud technologies and the internet services, people are getting more dependent on multi-party services, such as online meetings and classes, video and audio group calling and messaging, online conferences and webinars, and online gaming. To secure these multi-party communications, one of the most important components is the group key exchange (GKE). The existing GKE approaches are computationally expensive and do not offer scalability. These approaches only support small static groups to share a common secret key and do not properly address the situation of adding or removing group member(s). This is not acceptable for the multi-party communications with a large number of participants, especially when any participant(s) can join or leave the communications at any time. In this paper, we propose a secure, authenticated, and scalable group key exchange (SAS-GKE) that implements a constant-round contributory approach to generate the common secret key between any number of participants. SAS-GKE arranges all the participants in a three-tiered (depth = 2) m-ary tree structure that distributes the computational load between the participants in a balanced way. The proposed GKE utilizes public key authentication that prevents man-in-the-middle (MITM) attacks at every step of the group key exchange.
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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 | 0.001 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".