A Decentralized Security Framework for Data Aggregation and Access Control in Smart Grids
Bibliographic record
Abstract
We propose a decentralized security framework for smart grids that supports data aggregation and access control. Data can be aggregated by home area network (HAN), building area network (BAN), and neighboring area network (NAN) in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism uses attribute-based encryption (ABE) which gives selective access to consumer data stored in data repositories and used by different smart grid users. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Since RTUs are maintained in the substations they are well protected in control rooms and are assumed to be trusted. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute the keys which makes the approach robust. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and the first paper which addresses access control in smart grids.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".