文献类型：期刊文献

英文题名：Application Research of Data Mining Technology in Personal Privacy Protection and Material Data Analysis

作者：Liu, Jianguo[1];Zhou, Sheng[2]

第一作者：刘建国

通讯作者：Zhou, S[1]

机构：[1]Beijing Union Univ, Coll Appl Sci & Technol, Beijing, Peoples R China;[2]Wuhan Coll, Sch Finance & Econ, Wuhan, Hubei, Peoples R China

第一机构：北京联合大学应用科技学院

通讯机构：[1]corresponding author), Wuhan Coll, Sch Finance & Econ, Wuhan, Hubei, Peoples R China.

年份：2021

卷号：216

期号：1

起止页码：29-42

外文期刊名：INTEGRATED FERROELECTRICS

收录：;EI(收录号：20212710594666);Scopus(收录号：2-s2.0-85109066462);WOS:【SCI-EXPANDED(收录号:WOS:000668381300002)】；

语种：英文

外文关键词：Data mining technology; privacy protection technology; data processing; information protection

摘要：With the rapid development of information technology, the scale, scope and depth of database applications continue to expand, resulting in the phenomenon of "rich data and poor information." Misuse and misuse of data mining may lead to the leakage of user data, especially sensitive information. More and more people are worried about this, and even refuse to provide real data. Therefore, it is very necessary to study the application of data mining technology in personal privacy protection and material data analysis. The purpose of this article is to solve the problem of data privacy protection data mining. It mainly focuses on the research of privacy protection data mining algorithms. It uses Bayesian method analysis, theoretical analysis and logical analysis methods to carry out data mining technology in personal privacy protection and The application research in material data analysis shows that through the legal strengthening and management of data mining technology, the main reason is that data mining technology prohibits the illegal acquisition and use of other people's information on personal privacy, which can make the data mining data The security of the technology in privacy protection has been increased by 20%. In the analysis of material data, the data mining technology has greatly improved the number of processing and analysis capabilities compared to ordinary methods, which can increase the efficiency of material data analysis by 10%.