Lightweight Authentication and Privacy-Preserving Scheme for V2G Connections
Bibliographic record
Abstract
Vehicle-to-grid (V2G) connection allows a two-way electricity transmission between electric vehicles (EVs) and the power grid for achieving many known benefits. However, V2G connections suffer from certain security threats, such as to the EV's privacy and in authenticating the EV to the grid. In this paper, we propose a lightweight secure and privacy-preserving V2G connection scheme in which the power grid assures the confidentiality and integrity of exchanged information during (dis)charging electricity sessions and overcomes the EVs' authentication problem. The proposed scheme guarantees the financial profits of the grid and prevents EVs from acting maliciously. Meanwhile, EVs preserve their private information by generating their own pseudonym identities. In addition, the scheme maintains accountability for the electricity exchange trade. Furthermore, the proposed scheme provides these security requirements by lightweight overhead, as it diminishes the number of exchanged messages during (dis)charging sessions. Simulation results demonstrate that the proposed scheme significantly reduces the total communication and computation load for V2G connection, particularly for EVs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".