Abstract
Big data is massive and heterogeneous, along with the rapid increase in data quantity, and the diversification of user access, traditional database, and access control methods can no longer meet the requirements of big data storage and flexible access control. To solve this problem, an entity relationship completion and authority management method is proposed. By combining the weighted graph convolutional neural network and the attention mechanism, a knowledge base completion model is given. On this basis, the authority management model is formally defined and the process of multilevel trust access control is designed. The effectiveness of the proposed method is verified by experiments, and the authority management of knowledge base is more fine-grained and more secure.
Highlights
With the rapid development of the Internet, the current big data technology is characterized by large data volume, fast output speed, wide data types, and complex relationships among data
The previous data warehouse technology has been slightly insufficient to deal with the new problems in the current big data environment [10], such as the bulk of data, the diversity of data, and the irregularity of data structure in the current environment
In the access control model experiment, after the above classification based on the attention-weighted graph convolutional neural network, the knowledge base is divided into 12 categories of secondary authority and four categories of first-level authority, and role-permission allocation is carried out
Summary
With the rapid development of the Internet, the current big data technology is characterized by large data volume, fast output speed, wide data types, and complex relationships among data. It faces many difficulties and security risks in the process of data collection, data storage, data transmission, and data application [1,2,3]. Traditional big data security technology has been difficult to operate effectively in the big data environment [4,5,6]. By combining deep learning with access control, the model is formally defined, and the process of completing the knowledge graph and the design flow of multilevel trust access control are given; experiments verify the effectiveness of this method and realize more fine-grained and secure authority management of the knowledge graph
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