PPPA: A practical privacy-preserving aggregation scheme for smart grid communications
Bibliographic record
Abstract
Characterized by time-of-using pricing, better capacity, and usage planning, smart grid has attracted considerable attention in recent years. However, one of the research challenges in smart grid is the privacy issue due to too much sensitive and real-time user data involved. In this paper, we propose a practical privacy-preserving aggregation scheme, named PPPA, for secure smart grid communications. PPPA utilizes the lightweight cryptographic aggregation technique to achieve provable security guarantee, the differential privacy technique to achieve privacy preservation of each individual user, and the quad tree structure to achieve failure tolerance. For data communication from users to control center, data aggregation is conducted directly on ciphertexts at local gateway without decryption, and the aggregation results are reported by relays to the control center. Through extensive analysis, we demonstrate that PPPA can not only provide authentication and guarantee the data integrity, but also achieve strong privacy-preserving for each user, and thus it is feasible in practical smart grid scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".