Secure and Flexible Data Sharing With Dual Privacy Protection in Vehicular Digital Twin Networks
Bibliographic record
Abstract
Vehicular digital twin networks (VDTNs) offer great opportunities for driver safety enhancements. By leveraging digital twin (DT) technology, VDTNs can collect and analyze traffic data to optimize driving routes, and allow the out-of-field vehicles to share traffic data via their DTs. However, the real-time data sharing process over a public channel raises concerns about security and privacy. Existing data sharing schemes cannot be directly adopted for VDTNs because they rarely consider dual (data and identity) privacy, synchronization, and flexibility, while also imposing a significant cost on resource-limited entities. To address these challenges, we propose a secure and flexible data sharing scheme with dual privacy protection for VDTNs. In the proposed scheme, a signature of knowledge protocol is developed for protecting the vehicle’s real identity and ensuring authentication, smart contract algorithms are designed to assist in realizing accountability, and a verification control mechanism is devised for allowing the vehicle to flexibly share the traffic data. Additionally, DT with consistent states is capable of removing sensitive information from the shared data, which guarantees synchronization and data privacy. The security analysis demonstrates that the proposed scheme is resilient against potential security threats in VDTNs. Furthermore, the performance evaluation indicates that the proposed scheme not only outperforms the state-of-the-art schemes but also achieves feasible blockchain consumption and data authentication delay.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".