Research on Network Data Security Integration Based on Blockchain Technology
Bibliographic record
Abstract
With the rapid development of informatization technology, the security of network data is more and more emphasized.In this study, ECDSA digital signature algorithm and PBET consensus algorithm are adopted to construct a network data security model based on blockchain technology.The system in this paper consists of three functional modules: application interaction client, federation chain Fabric module and data storage module DHT, which are further logically divided into ive parts: initialization, identity registration, uploading data, querying data and permission revocation.The average CPU occupation of each component of the system ranges from 0.02% to 39.96%, which consumes low resources, and the maximum value of the time used by the system for data encryption and decryption and signature authentication is no more than 41ms, which is a relatively fast operation speed, and it can support the operation of the network data security system, and the designed system has relatively high security in resisting the attack of the authentication process, and it utilizes the decentralized characteristics of blockchain to resist the attacks of the distribution process, and it utilizes the blockchain to resist the attacks of the distribution process.Centrality to resist distributed denial of service (DDoS) attacks and replay attacks.This study provides lessons and references for the application of blockchain technology in network data security.
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How this classification was reachedexpand
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".