Privacy-Preserving Proxy Re-Encryption With Decentralized Trust Management for MEC-Empowered VANETs
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".