Lightweight Group Authentication for Decentralized Edge Collaboration
Bibliographic record
Abstract
Due to tedious interactions and intensive computations, existing group authentication methods suffer from long latency and high overhead for simultaneously identifying multiple devices associated with one common task. Considering the growing importance of decentralized edge collaboration, this article proposes a lightweight group authentication scheme by utilizing the collaboration process information. Specifically, the edge devices in the same task group generate tokens for authentication based on knowledge from previous rounds of learning-based collaboration. We develop both the random token generation and privacy-preserving token generation strategies for achieving quick authentication and security enhancement, respectively. The generated tokens are easy for the legitimate group members to verify but extremely difficult for attackers to predict. A group authentication protocol is proposed for edge devices to verify the others' identities based on their tokens simultaneously. The proposed scheme achieves lightweight authentication without extra security information in addition to generating and distributing any key (secret), resulting in both enhanced security and decreased cost. More importantly, the proposed scheme enhances security and provides continuous protections for the decentralized edge collaboration by utilizing its natural update of authentication knowledge before the next round of collaboration begins. Our simulation study considers an example of decentralized edge collaboration and verifies the proposed scheme.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".