On Secure and Efficient Data Sharing for Smart Grids: An Anti-Collusion Scheme
Bibliographic record
Abstract
High volumes of real-time energy consumption data are generated by smart meters each day, which may create tremendous values if shared to third parties, e.g., government agencies, real estate agents, and travel agencies, etc. However, significant security and privacy challenges are always in place in case these sensitive digital assets are directly shared outside the grid utilities without any protection. It is, therefore, vital to guarantee the data security and privacy while maintaining its values. To meet this gap, in this paper we propose a secure and efficient data sharing scheme with anti-collusion for smart grids. In this scheme, we devise a privacy-preserving data acquisition algorithm for smart meters based on a modified Paillier cryptosystem, and also an anti-collusion proxy re-encryption algorithm for the control center & cloud server to achieve secure energy consumption data sharing of its mean and variance. Importantly, the proposed scheme is also designed to support customer identity preservation while requesting data sharing services. Security analysis strictly demonstrate the security of the proposed scheme, and extensive experiments validate the high efficiency of the proposed scheme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".