Author
Listed:
- Lijun Zu
(School of Computer Science, Fudan University, Shanghai 200433, China
Institute of Financial Technology, Fudan University, Shanghai 200433, China
China UnionPay Co., Ltd., Shanghai 201210, China)
- Wenyu Qi
(Huawei Technologies Co., Ltd., Nanjing 210012, China)
- Hongyi Li
(School of Computer Science, Fudan University, Shanghai 200433, China)
- Xiaohua Men
(China UnionPay Co., Ltd., Shanghai 201210, China)
- Zhihui Lu
(School of Computer Science, Fudan University, Shanghai 200433, China
Institute of Financial Technology, Fudan University, Shanghai 200433, China)
- Jiawei Ye
(School of Computer Science, Fudan University, Shanghai 200433, China)
- Liang Zhang
(Huawei Technologies Co., Ltd., Nanjing 210012, China)
Abstract
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.
Suggested Citation
Lijun Zu & Wenyu Qi & Hongyi Li & Xiaohua Men & Zhihui Lu & Jiawei Ye & Liang Zhang, 2024.
"UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment,"
Future Internet, MDPI, vol. 16(3), pages 1-24, March.
Handle:
RePEc:gam:jftint:v:16:y:2024:i:3:p:102-:d:1358832
Download full text from publisher
References listed on IDEAS
- Lijun Zu & Hongyi Li & Liang Zhang & Zhihui Lu & Jiawei Ye & Xiaoxia Zhao & Shijing Hu, 2023.
"E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios,"
Future Internet, MDPI, vol. 15(9), pages 1-17, August.
- Tong Yi & Minyong Shi, 2015.
"Privacy Protection Method for Multiple Sensitive Attributes Based on Strong Rule,"
Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, August.
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