PMQC: A privacy-preserving multi-quality charging scheme in V2G network
Bibliographic record
Abstract
Multi-quality charging, which provides the electric vehicles (EVs) with multiple levels of charging services, including quality-guaranteed service (QGS) and best effort service (BES), can guarantee the charging service quality for the qualified EVs in vehicle-to-grid (V2G) network. To perform the multi-quality charging, the evaluation on the EVs attributes is necessary to determine which level of charging service can be offered to this EV. However, the EV owner's privacy such as real identity, lifestyle, location, and sensitive information in the attributes may be disclosed during the evaluation and authentication. In this paper, we propose a privacy-preserving multi-quality charging (PMQC) scheme in V2G network to evaluate the EVs attributes, authenticate its service eligibility and generate its bill without revealing the EVs private information. Specifically, we propose an evaluation mechanism on the EVs attributes to determine its charging service quality. With attribute based encryption, PMQC can prevent the EVs attributes from being disclosed to other entities during the evaluation. In addition, PMQC can authenticate the EV without revealing its real identity. Security analysis demonstrates that the EVs privacy mentioned above can be preserved by PMQC. Performance evaluation results show that PMQC can achieve higher efficiency in authentication compared with other schemes in terms of computation overhead.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".