Design and Implementation of a Secure Storage and Traceability System for Financial Documents Based on Blockchain Technology
Bibliographic record
Abstract
As the digital economy develops, the use of digital methods for the storage of inancial documents is being commonly adopted.To protect the data security of inancial documents and ensure the traceability of data, this article designed a secure storage and traceability system for inancial documents based on blockchain.Firstly, a blockchain platform with a private chain that had better security and scalability was selected, and the data structure and smart contract design were then carried out.Secondly, an identity authentication and permission management mechanism was established, and data storage and transaction processing modules were designed.Asymmetric encryption was used to secure data, and the digital signatures were combined to ensure the integrity of inancial documents.Finally, the traceability function was achieved through the immutability of blockchain technology.The task of storage and traceability was accomplished by designing a secure storage and traceability system for inancial documents in conjunction with the blockchain technology.In the experiment, it only took about 50 seconds to process traceability tasks with an interval of 50 people, which was shorter than the traditional system's 180 seconds; it also maintained an accuracy rate of over 90% in traceability tasks with an interval of 100 people; in the face of 1000 network attacks in a short period of time, the inancial management system based on blockchain technology was only invaded 20 times, while the traditional inancial system was invaded 200 times.This system, in terms of time, traceability accuracy, and data security, were all improved over the traditional system.The design of a secure storage and traceability system for inancial documents based on blockchain technology is conducive to strengthening the security of data and the accuracy of traceability.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".