Open AccessData Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network
Author(s) -
Qianyi Zhang,
Chi Zhang,
Jiaming Ni,
Xuqiang Wang,
Yao Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012107
Subject(s) - artificial neural network , data mining , computer science , sensitivity (control systems) , entropy (arrow of time) , artificial intelligence , machine learning , pattern recognition (psychology) , engineering , physics , quantum mechanics , electronic engineering
For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.