Puncturable Signature and Applications in Privacy-Aware Data Reporting for VDTNs
Bibliographic record
Abstract
In vehicular digital twin networks (VDTNs), digital twin (DT) can assist the vehicle in data handling and report traffic data to the management server, thereby providing enhanced and scalable services for intelligent transport systems. However, the reported data may suffer from forgery and eavesdropping attacks due to the transmission on the open channel. In addition, a critical threat in VDTNs is the physical vehicle capture attack, namely, an adversary is capable of compromising the vehicle to obtain the current secret key, which can break the reliability of historical reported data and make the services provided by DT unavailable. Puncturable signature (PS) is a promising solution to eliminate these concerns, despite that the existing PS constructions have non-negligible false-positive errors and impose a significant cost on practical deployments. In this paper, we design a novel PS and apply it to privacy-aware data reporting protocol (PA-DRP) for VDTNs. Specifically, the designed PS adopts a derivationbased way to achieve puncturing functionality, which is free from false-positive errors while extremely reducing the storage overhead of the secret keys. Meanwhile, we employ the designed PS to construct PA-DRP that enjoys authentication and forward security. Additionally, PA-DRP not only allows DT to remove privacy-sensitive information from the signed data but also provides fuzzy identity for protecting the real identity of the vehicle. Furthermore, the security analysis and performance evaluation demonstrate that the designed PS and PA-DRP not only can withstand various security and privacy assaults for VDTNs but also are efficient and practical.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".