Research on Power Information Security Protection and Big Data Privacy Protection in Internet Communication
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 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.
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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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