Developments and Applications of Data Deidentification Technology under Big Data

被引:0
|
作者
Hung-Li Chen [1 ]
Yao-Tung Tsou [1 ]
Bo-Chen Tai [2 ]
Szu-Chuang Li [2 ]
Yen-Nun Huang [2 ]
Chia-Mu Yu [3 ]
Yu-Shian Chiu [4 ]
机构
[1] the Department of Communications Engineering,Feng Chia University
[2] the Research Center for Information Technology Innovation,“Academia Sinica”
[3] Department of Computer Science and Engineering, Chung Hsing University
[4] the Data Analytics Technology &Applications,Institute for Information Industry
关键词
Deidentification; differential privacy;
D O I
暂无
中图分类号
TP309 [安全保密]; TP311.13 [];
学科分类号
081201 ; 0839 ; 1201 ; 1402 ;
摘要
In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy.As such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data.As a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent years.In this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods.Furthermore,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.
引用
收藏
页码:231 / 239
页数:9
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