PARK: A privacy-preserving aggregation scheme with adaptive key management for smart grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it