Investigating Public-Key Certificate Revocation in Smart Grid
Bibliographic record
Abstract
The public key cryptography (PKC) is essential for securing many applications in smart grid. For the secure use of the PKC, certificate revocation schemes tailored to smart grid applications should be adopted. However, little work has been done to study certificate revocation in smart grid. In this paper, we first explain different motivations that necessitate revoking certificates in smart grid. We also identify the applications that can be secured by PKC and thus need certificate revocation. Then, we explain existing certificate revocation schemes and define several metrics to assess them. Based on this assessment, we identify the applications that are proper for each scheme and discuss how the schemes can be modified to fully satisfy the requirements of its potential applications. Finally, we study certificate revocation in pseudonymous public key infrastructure (PPKI), where a large number of certified public/private keys are assigned for each node to preserve privacy. We target vehicles-to-grid communications as a potential application. Certificate revocation in this application is a challenge because of the large number of certificates. We discuss an efficient certificate revocation scheme for PPKI, named compressed certificate revocation lists (CRLs). Our analytical results demonstrate that one revocation scheme cannot satisfy the overhead/security requirements of all smart grid applications. Rather, different schemes should be employed for different applications. Moreover, we used simulations to measure the overhead of the schemes.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".