Vehicular Edge-Based Approach for Optimizing Urban Data Privacy
Bibliographic record
Abstract
The rapid progress of the artificial intelligence (AI) sector has greatly impacted vehicular edge components (VECs) in the vehicular ad hoc network (VANET). Various AI applications, including automatic driving, preaccident alerts, and video broadcasting, have become essential to meet VANET’s diverse requirements. However, implementing these applications in the resource-constrained urban sensing environment poses challenges. To overcome this, we proposed a novel approach that partitions resource-intensive ciphertext-policy attribute-based decryption (CP-ABE) tasks based on ciphertext (CT) policy into sub-CTs using machine learning. Our technique, CT-distribution DE (CD-DE), utilizes differential evolution (DE) to decrypt CP-ABE tasks on VECs. It includes a selection algorithm that allows the data owner vehicle to choose VEC components for decryption operations. Compared to widely used techniques such as particle swarm optimization (PSO) and genetic algorithm (GA), CD-DE offers lower overhead and provides accurate near-optimal solutions across most scenarios. Our study demonstrates the effectiveness of CD-DE in improving the efficiency and accuracy of resource-intensive CP-ABE tasks in VANETs. The proposed approach addresses the challenges posed by limited resources, bandwidth, and workload constraints in the urban sensing environment. By enabling efficient implementation of AI-based applications in vehicles within urban environments, our approach holds promise for enhancing VANETs’ capabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".