PARK: A privacy-preserving aggregation scheme with adaptive key management for smart grid
Bibliographic record
Abstract
Smart Grid, as one kind of promising sustainable power systems, can rely on two-way communications and emerging smart meters to intelligently control the residential electricity usage. However, due to the inherent open communication media as well as the limited communication, computation and storage capabilities of smart meters, security concerns raise and hinder the further flourish of smart grid. In this paper, we propose a privacy-preserving aggregation (PARK) scheme with adaptive key management and revocation, to prevent user's data from being disclosed to untrusted entities in smart grid. Specifically, we first investigate a lightweight aggregation scheme with efficient aggregate authentication, which protects the individual user's data from disclosure to the untrusted aggregator. Furthermore, we propose an adaptive key management mechanism with effective revocation, where users can automatically update their encryption keys if no user joins or departs from the system. The expiry time of the key is determined by user's reputation for the adaptive key management. Finally, the security analysis demonstrates that the PARK can achieve privacy preservation, forward and backward secrecy at the same time, while the performance evaluation shows that the PARK consumes reasonable costs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".