Efficient scheme for secure and privacy-preserving electric vehicle dynamic charging system
Bibliographic record
Abstract
The dynamic charging technology will enable Electric Vehicles (EVs) to charge their batteries while moving. Special charging pads will be placed on the roads to charge the EVs through the magnetic induction. The dynamic charging system should communicate with the EVs to only charge authorized vehicles and ensure payment integrity. This communication should be secured and should not leak any private information of the EV drivers, especially location information. In this paper, we propose an efficient scheme to secure the dynamic charging system and preserve the privacy of the drivers. The scheme uses a combination of different cryptosystems to achieve security and privacy. Anonymous coins are used to ensure anonymous payment and authentication. We also developed a hierarchical authentication scheme that uses efficient cryptosystems like hashing and Exclusive-OR operations. In addition, the proposed scheme considers the characteristics of the dynamic charging system such as the large number of pads having limited computational resources and the short contact time between EVs and pads due to the high speed of EVs. Our analysis demonstrates that the proposed scheme is secure and can preserve privacy. In addition, our measurements confirm that the proposed scheme is efficient.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".