Artificial Intelligence-based Financial Big Data Information Security and Local Risk Prevention and Control
Bibliographic record
Abstract
As a new product of artificial intelligence, big data is widely used in daily life.Due to its appearance, people's lives are more convenient and efficient, but at the same time, there are certain security risks, namely the leakage of private information, especially the financial information problem brought about by financial informatization has a more serious leakage problem.In order to effectively reduce the problems caused by the leakage of financial information privacy, this paper attempted to establish a model of related protection measures for financial big data information security by establishing a three-dimensional encrypted information model of big data or by using differential privacy method and using their own.The three-dimensional encrypted information model of big data overcame the defect that financial information is easy to be broken, while the differential privacy model overcame the defect of inaccurate protection of financial information, both of which can play a better protective role in different applications.The experimental results showed that in the process of accessing financial data information, with the increase of access frequency, the number of sensitive locations changes from 40 to 46.This also meat that a non-sensitive position becomes a sensitive position, which blurs the original sensitive position and achieves the effect of protecting the real sensitive position.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".