An Efficient Elliptic Curve Cryptography‐Based Secure Communication with Privacy Preserving for Autonomous Vehicle
Bibliographic record
Abstract
Internet of Vehicles can improve driving and riding experience, provide information needs, reduce environmental pollution, and improve transportation efficiency, thereby promoting the rapid development and application of intelligent transportation. Especially through the advantages of the rapidity of information exchange and the flexibility of real‐time data processing, an autonomous vehicle can provide barrier‐free, safe, and sustainable transportation. At present, the rapid progress of the Internet of Things has promoted the continuous development of a fully autonomous vehicle. However, the autonomous vehicle network uses wireless communication technology, and the openness of its communication channel makes the communication process vulnerable to various security attacks. Therefore, this paper proposes a practical and efficient secure communication in the autonomous vehicle. This scheme utilizes elliptic curve cryptography, which can protect the security of multiparty communication between vehicle, cloud server, and user with lower computation and communication overhead. This paper provides security verification for the scheme by using Scyther. An informal security analysis shows that this scheme can resist multiple attacks. Through a comparative analysis and performance evaluation of the schemes, we found that the scheme improves security while maintaining efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".