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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120023

    Title: Developments and Applications of Data Deidentification Technology Under Big Data
    Authors: Chen, Hung-Li;Tsou, Yao-Tung;Tai, Bo-Chen;Li, Szu-Chuang;Huang, Yennun;Yu, Chia-Mu;Chiu, YuShian
    Keywords: Deidentification;differential privacy
    Date: 2017-09
    Issue Date: 2021-03-05 12:10:18 (UTC+8)
    Abstract: 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.
    Relation: Journal of Electronic Science and Technology 15(3), p.231-239
    DOI: 10.11989/JEST.1674-862X.60804064
    Appears in Collections:[Graduate Institute & Department of Information and Communication] Journal Article

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