A Lightweight Lattice-Based Homomorphic Privacy-Preserving Data Aggregation Scheme for Smart Grid
Bibliographic record
Abstract
Consumer privacy and consumption confidentiality and integrity are the main security concerns for smart grid connection with the residential electricity consumers. This paper proposes a lightweight privacy-preserving electricity consumption aggregation scheme that exploits lightweight lattice-based homomorphic cryptosystem. In the proposed scheme, smart household appliances aggregate their readings without involving the smart meter. Although smart meters or the intermediate base station cannot decrypt this aggregated consumption, they can validate the message's authenticity. The proposed scheme also investigates the impact of different types of smart appliances on the home area network's overhead. The total communication and computation load for the proposed scheme is trivial and tolerable by different parties in the connection, i.e., smart appliances, smart meters, and the base station. In addition, the deployed cryptosystem, which depends on simple arithmetic operations, can further reduce the computation duty for smart appliances. Simulation results and security analysis show that our proposed scheme guarantees consumers privacy, and messages authenticity and integrity, with lightweight communication and computation complexity.
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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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".