PrivGrid: Privacy-Preserving Individual Load Forecasting Service for Smart Grid
Bibliographic record
Abstract
Smart meter-based individual load forecasts are more and more widely deployed to serve smart grid and home energy management. Customary load forecasting systems collect a massive amount of fine-grained electrical data from people’s smart meters in plaintext, inevitably raising privacy concerns and even anti-smart-meter initiatives. Current privacy solutions either compromise accuracy and efficacy or require the redeployment of trusted infrastructure. In this paper, we present PrivGrid, the first systematic solution for smart grids that collects, clusters, trains, and forecasts customers’ load data in a privacy-preserving way. Moreover, we highlight the technical contribution of our building block: a novel and fast arithmetic multiplication triple via secure inner product protocol outperforms the existing methods and may be included in other privacy computing modules. Then, we develop efficient secure protocols to enable the arithmetic operations of individual load forecasting in a server-aided model and utilize the best alternatives to nonlinear functions. Besides, aggregating all of our individual forecasts can produce a more accurate estimate of the system-level load than the typical aggregate technique. We rigorously prove that the servers cannot obtain the user’s historical load data and short-term load forecast values while providing services. PrivGrid is also tested on real residential smart meter data to show its efficiency, and the relevant code has been made available to the community for further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".