Privacy-Preserving Proxy Re-Encryption With Decentralized Trust Management for MEC-Empowered VANETs
Why this work is in the frame
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Bibliographic record
Abstract
Multi-access edge computing (MEC) technology is widely deployed at the edge of Vehicular Ad hoc Networks (VANETs) to enhance their communication and computational capabilities. However, existing security and privacy preservation solutions for MEC applications in VANETs face several challenges, such as the risk of privacy exposure of vehicle authentication, increased overhead due to cryptographic algorithms, as well as resource occupation and malicious attacks on edge servers. In this paper, we propose an aggregated security solution for the confidential, efficient, and trustworthy sharing of data while safeguarding the privacy of vehicle identities. Firstly, we present a broadcast proxy re-encryption scheme based on cubic spline interpolation that ensures the security of the VANET system and the identity privacy of large-scale vehicles. The re-encryption system is designed to prioritize the reduction of re-encryption computation time rather than focusing on the sizes of re-encryption keys and ciphertexts. We further model the aggregation overhead in terms of communication and computation. Additionally, we propose an efficient protocol based on Practical Byzantine Fault Tolerant (PBFT) consensus to facilitate decentralized trust management for edge proxy servers. Our security analysis demonstrates that the proposed scheme satisfies the security and confidentiality requirements for data sharing in VANETs. Finally, we provide extensive simulations that reveal the performance and effectiveness of our solution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it