Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (7): 116-121.DOI: 10.3778/j.issn.1002-8331.1812-0081

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Non-interactive Queries Differential Privacy Protection Model in Big Data Environment

XU Bin, LIANG Xiaobing, SHEN Bo   

  1. 1.China Electric Power Research Institute, Beijing 100192, China
    2.State Key Laboratory of Information Security, Institute of Information Engineering, Beijing 100093, China
    3.School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2020-04-01 Published:2020-03-28



  1. 1.中国电力科学研究院有限公司,北京 100192
    2.中国科学院信息工程研究所 信息安全国家重点实验室,北京 100093
    3.中国科学院大学 网络空间安全学院,北京 100049


In the big data environment, non-interactive differential privacy can not accurately provide and deal with a large number of queries. A privacy protection data query model based on maximum information coefficient and machine learning is proposed. Firstly, the data with low correlation is selected as the training sample set by using the maximum information coefficient of the original data set, and then combined with the parallel combination property of differential privacy to obtain the privacy-protected training sample set. Finally, the linear regression algorithm is used to train the sample. The differential privacy protection prediction model answers the current and a large number of unknown queries. The experimental results show that the proposed model improves the efficiency of query processing while improving the utility of published data.

Key words: differential privacy, maximum information coefficient, privacy protection, range query



关键词: 差分隐私, 最大信息系数, 隐私保护, 范围查询