Security-Enhanced Data Aggregation against Malicious Gateways in Smart Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In smart grid, to monitor, predict and control the power consumption in real time, energy usage data have to be periodically collected through publicly accessible communication channels, and are stored in a centralized operation center. However, electricity consumption data may disclose the privacy information of users. Therefore, protecting privacy of users and validity of power usage reports becomes a crucial security issue. In this paper, we propose a security-enhanced data aggregation scheme for smart grid communications based on homomorphic cryptosystem, trapdoor hash functions and homomorphic authenticators. Our scheme can achieve data confidentiality and integrity against the malicious aggregator (e.g. gateway), meaning that the aggregator is not able to access users' private information or corrupt the power consumption reports during the aggregation process. Through extensive analysis, we demonstrate that our scheme can resist potential threats and be proved secure under cryptographic hard assumptions. It has less computational and communication overheads than existing approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it