Efficient authentication scheme for cross-trust domain of IoV based on double-layer shard blockchain
Bibliographic record
Abstract
To solve the problems of poor scalability, slow synchronization of authentication information, and high authentication overhead in cross-trust domain message authentication in the Internet of vehicles (IoV), an efficient authentication scheme for cross-trust domain of IoV based on a double-layer shard blockchain was proposed.A double-layer shard blockchain architecture was designed for lots of cross-trust domain message authentication by constructing blockchains on different entity levels in all domains to improve the scalability of the system and ensure secure and efficient sharing of cross-domain information.A blockchain sharding method based on the Metis graph partitioning algorithm for IoV was proposed to balance loads of each shard and adapt to the uneven distribution of authentication information on different road segments in IoV, thereby improving the efficiency of synchronizing a large number of authentication information on the chain.A batch authentication scheme based on certificateless public-key cryptography (CL-PKC) was proposed, which reduced the authentication overhead of cross-domain messages by enabling batch authentication of messages from different trust domains.Experimental results show that the proposed scheme effectively improves the authentication efficiency of cross-trust domain messages.Compared with other schemes, the proposed scheme reduces the computational overhead of a large number of cross-domain message authentication by more than 26.4%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".