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 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".