Laws and Regulations tell how to classify your data: A case study on higher education

Yang, Min; Tan, Liuyan; Chen, Xingshu; Luo, Yonggang; Xu, Zuo-Feng; Lan, Xiao

doi:10.1016/j.ipm.2022.103240

Cited by 5 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they overlooked the potential risk stemming from the exposure of personal sensitive data under unforeseen circumstances. Subsequently, Yang M presented the Gen-DT scheme [26], which leverages legal statutes to establish an external knowledge base. They employed a generalization-enhanced decision tree algorithm to categorize data into regulatory and non-regulatory types.…”

Section: Data Classification and Grading Frameworkmentioning

confidence: 99%

UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment

Zu,

Qi,

et al. 2024

Future Internet

View full text Add to dashboard Cite

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.

show abstract

Section: Data Classification and Grading Frameworkmentioning

confidence: 99%