Developments and applications of data deidentification technology under big data

Hung Li Chen, Yao Tung Tsou*, Bo Chen Tai, Szu Chuang Li, Yen Nun Huang, Chia Mu Yu, Yu Shian Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)231-239
Number of pages9
JournalJournal of Electronic Science and Technology
Issue number3
StatePublished - 1 Sep 2017


  • Deidentification, differential privacy

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