Unstructured Text Data Security Attribute Mining Method Based on Multi-Model Collaboration

Abstract

Access control is a critical security measure to ensure that sensitive information and resources are accessed only by authorized users. However, attribute-based access control in the big data environment faces challenges such as a large number of entity attributes, poor availability, and difficulty in manual labeling. In this paper, we focus on the problem of mining and optimizing security attributes of unstructured data resources and propose a method for mining security attributes of unstructured textual data based on multi-model collaboration. First, we utilize unsupervised methods to extract candidate attributes from textual resources, and then weight the results of multiple methods using rough set theory to obtain the optimal result. Second, considering various factors including the text itself and the candidate attributes, we construct a feature vector consisting of 45 categories to represent the candidate attributes. Third, we employ a multi-model voting method to collaboratively train the attribute mining model and obtain the security attributes of textual resources. Finally, based on HowNet, we optimize the security attributes to achieve automated and intelligent mining of access control data resource security attributes, providing an attribute foundation for precise access control. The experiments indicate that the attribute mining precision rate of the method proposed in this paper can reach up to 92.36%, F1-score can reach up to 82.51%. The attribute scale can be compressed to 69.59% of its original size after optimization. This method has a greater advantage over other methods and can provide attribute support for access control of large data resources.