Research on Security Protection Strategies for Power Information Data Based on Big Data
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
This paper conducts a systematic study on multimedia communication security and big data privacy protection issues faced during the internet-based transformation of the power system. By analyzing new attack surfaces, vulnerability characteristics, and privacy protection needs in the power internet communication environment, a "proactive defense-privacy enhancement" dual-drive technology system is constructed. On the security protection level, a data security transmission scheme based on domestic cryptographic algorithms, a zero-trust dynamic access control mechanism, a big data situation awareness platform, and a collaborative emergency response system are proposed. On the privacy protection level, the innovative fusion of differential privacy and federated learning technologies is adopted to establish a privacy protection framework covering the entire data lifecycle. Empirical research shows that this system can reduce the incidence of security events by more than 75% and achieve controllable privacy while ensuring business real-time performance, effectively solving the balance problem between security protection and privacy protection in the context of power big data, and providing technical support and practical paths for building a new power system security ecosystem.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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