Sequential tree recognition method of sensitive data in energy big data center based on rule matching

Abstract

In this paper, the sequential tree recognition method of sensitive data in energy big data center based on rule matching is studied, to accurately identify sensitive data in energy big data center, and improve the operation security of energy big data center. The RETE rule matching algorithm is used to match the sensitive data rules of the energy big data center. The algorithm automatically finds the optimal rete topology, reduces the join intermediate node data, and realizes rule matching. The data cut points after rule matching are divided into balanced cutting points and unbalanced cutting points. The maximum sorting mutual information only exists at the unbalanced cut points. The ordered decision tree can be constructed by traversing the unbalanced cutting points. The data to be identified can be retrieved in the form of data flow to obtain the word frequency, regional information and sensitivity of sensitive words, and the sensitive data can be identified according to the sensitivity calculation results. The experimental results show that the proposed method can effectively identify the sensitive data of energy big data center with high recognition accuracy, and can be applied to the practical application of energy big data center.