Robust privacy-preserving authentication scheme for communication between electric vehicle as power energy storage and power stations
Bibliographic record
Abstract
The concept of Electric Vehicle as Power Energy Storage has gained much attention from the research community and market recently. The increasing capacity of the power storages in the electric vehicles (EV) motivates this concept and makes it more feasible. However, the privacy of the customers can be compromised by tracing the stations that an EV has been connected to during a period of the time. The stations that are providing power charging as well as purchasing the power back from EVs can be owned by third party businesses. EVs should be authenticated through these stations in order to give or receive appropriate credit for the power. In this paper, we identify potential privacy issues and propose a robust privacy-preserving authentication scheme for communication of the EV and the station to prevent customer information leakage. In our approach, the EV and the station communication utilizes pseudonym of the EV, in which only the smart grid server (a trusted entity) can map the pseudonym to the real vehicle identity and provide the identity management. The pseudonym of an EV changes when the EV moves from one station to another, which prevents the adversary from tracing foot prints of the EV. Our analysis shows that our model is robust enough to make sure the privacy of the customers is fully preserved, and at the same time, it is efficient by consuming very limited resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".