Chaotic encryption method for network privacy data based on dynamic data mining

In order to improve the security of network privacy data, this paper proposes a new chaotic encryption method of network privacy data based on dynamic data mining. In this method, SOM neural network is used for dynamic training and mining of network privacy sample data. Based on the results of dynamic training and mining, the quantitative coding characteristics of network privacy data are obtained by using logistics chaotic mapping. Under the constraint of association mapping, the chaotic encryption results of network privacy data are output to complete the chaotic encryption of network privacy data. Experimental results show that, compared with the traditional chaotic encryption method, the proposed method has higher security factor and wider encryption range, and the highest encryption security factor can reach 0.91. Therefore, this method can effectively ensure the security of network privacy data.