Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering
Bibliographic record
Abstract
Smart grid enables two-way communications between operation centers and smart meters to collect power consumption and achieve demand response to improve flexibility, reliability, and efficiency of electricity system. However, power consumption data may contain users' privacy, e.g., activities, references, and habits. Many smart metering schemes have been proposed utilizing homomorphic encryption for users' privacy preservation. Unfortunately, some abnormality of smart meter reading, e.g., caused by electricity theft, cannot be discovered since data is encrypted. Meanwhile, operation centers could become curious in reality. To address the above issues, we propose a new privacy-preserving smart metering scheme for smart grid, which supports data aggregation, differential privacy, fault tolerance, and range-based filtering simultaneously. Specifically, we extend lifted ElGamal encryption to aggregate users' consumption reports at the gateway to reduce communication overhead, while supporting fault tolerance of malfunctioning smart meters effectively. We also leverage zero-knowledge range proof to filter abnormal measurements caused by electricity theft or false data injection attacks without exposing individual measurements. In addition, our scheme can resist differential attacks, by which the curious operation center can violate users' privacy through comparing two aggregations of the similar data set. Finally, we discuss the properties of the proposed scheme and evaluate its performance in terms of security and efficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 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".