Privacy Protection in Information and Communication Technology Applications Based on Big Data
Bibliographic record
Abstract
With the advent of the information age, big data has become an important force driving the rapid development of information and communication technology, while also bringing huge challenges to user privacy and security. This article is based on various privacy protection methods such as data anonymity, differential privacy, and ciphertext. Through quantitative and empirical research, the application of big data in information communication was deeply explored. Therefore, this article focused on how to efficiently apply data in network security while ensuring its availability and accuracy. A new data anonymity model was designed to address the issue of data anonymity, which can maximize the protection of user privacy and the availability of user data; the impact of different noise addition strategies on the accuracy of data analysis in differential security systems was studied; on this basis, a study was conducted on the combination of multiple encryption methods to improve their security in storage and transmission. In the latest data security assessment, data encryption led with a high score of 9.5, demonstrating its outstanding performance in protecting data security. This study provided a new approach and method for China's privacy protection in the field of information and communication, which can enhance the country's credibility in the field of public information and communication.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".