Hidden Encryption Algorithm of Big Data Characteristics of Submitted Information Based on Improved Association Rules
The security of big data of submitted information is directly related to the accuracy of data mining of"digital discipline inspection cloud platform".Once it is leaked or tampered,the reliability of discipline inspection work will be great-ly reduced.Therefore,a feature hiding encryption algorithm of submitted information big data based on improved association rules is proposed.Manually obtain the big data of the submitted information,connect the heterogeneous database in real time through the connection converter,and automatically correct the data that does not meet the specification requirements,inclu-ding missing data filling,outlier processing and data standardization.The improved genetic algorithm is used to improve the Apriori algorithm to mine the hidden association rules between data items or attributes,and identify the characteristics of big data of submitted information through association rules.Taking the big data characteristics of the submitted information as the input,and using the weight matrix between the neural network levels as the key,the plaintext data is transformed into ciphertext data.The results show that under the application of the encryption algorithm studied,the correlation between ci-phertext and plaintext after the encryption of"three public funds"expenditure data is between 0～0.2,which shows that the encryption effect of the studied algorithm is good and ensures the security of big data of submitted information.
improved association rulesApriori algorithmsubmitted information big datafeature recognitionneural networkhidden encryption algorithm
国家科技期刊平台
NSTL
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