AD‐ASGKA – authenticated dynamic protocols for asymmetric group key agreement
Bibliographic record
Abstract
Abstract Asymmetric group key agreement is a cryptographic primitive allowing a group of users to negotiate a common public encryption key while each of them holds a different secret private decryption key. Anyone (including outsiders) with the public encryption key can send encrypted messages to the group members, and then the group members can decrypt the messages. Authenticated key agreement protocols authenticate the identities of users to ensure that only the intended group members can establish a session in which the group members can communicate with each other. Dynamic asymmetric group key agreement concerns about the scenarios such as ad hoc networks in which the group members may join or leave at any given time. In this paper, we propose a one‐round authenticated dynamic protocol for symmetric group key agreement. For efficiency reasons, we employ the identity‐based public‐key cryptography (IB‐PKC) to authenticate users rather than the public key infrastructure and the certificate‐less public‐key cryptography. Our analysis shows that the proposals in the paper can resist active attacks and meet many desirable security attributes. Besides, our protocol allows users to join or leave the group at the same time. Furthermore, our protocol is round‐optimal and has a quite good performance as compared with previous works. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".