التفاصيل البيبلوغرافية
| العنوان: |
UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment. |
| المؤلفون: |
Zu, Lijun, Qi, Wenyu, Li, Hongyi, Men, Xiaohua, Lu, Zhihui, Ye, Jiawei, Zhang, Liang |
| المصدر: |
Future Internet; Mar2024, Vol. 16 Issue 3, p102, 24p |
| مصطلحات موضوعية: |
EDGE computing, CLASSIFICATION of books, DIGITAL transformation, BANKING industry, APPLICATION program interfaces, LABOR costs, CLOUD computing, CLOUD storage |
| مستخلص: |
The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model's capabilities to encompass unstructured data classification and grading, broadening the scope of application. [ABSTRACT FROM AUTHOR] |
|
Copyright of Future Internet is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder