Research on Power Information Security Protection and Big Data Privacy Protection in Internet Communication
Bibliographic record
Abstract
This paper conducts a systematic study on the multimedia communication security and big data privacy protection problems faced during the Internet-based transformation of power systems. It analyzes the unique systematic security risks, new attack surfaces, and privacy protection requirements in the power Internet communication environment, and constructs a "proactive defense-privacy enhancement" dual-drive technology system. On the security protection level, it proposes a data encryption transmission scheme based on domestic cryptographic algorithms, a zero-trust dynamic access control mechanism, a multimedia steganography detection method, and a collaborative emergency response system. On the privacy protection level, it innovatively adopts dynamic anonymization and differential privacy fusion technology, a federated learning framework, and a full lifecycle security management system. Empirical application through a provincial power company shows that the system can reduce the incidence of security events by more than 75% and achieve privacy control while ensuring business real-time performance. The research provides a systematic solution for building a secure and reliable power Internet communication environment, and has important practical value for promoting the construction of a new type of power system.
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.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".