Developments and Applications of Data Deidentification Technology Under Big Data
學年 106
學期 1
出版（發表）日期 2017-09-01
作品名稱 Developments and Applications of Data Deidentification Technology Under Big Data
作品名稱（其他語言）
著者 Hung-Li Chen; Yao-Tung Tsou; Bo-Chen Tai; Szu-Chuang Li; Yennun Huang; Chia-Mu Yu; YuShian Chiu
單位
出版者
著錄名稱、卷期、頁數 Journal of Electronic Science and Technology 15(3), p.231-239
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.
關鍵字 Deidentification;differential privacy
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Yao-Tung Tsou
審稿制度 是
國別 CHN
公開徵稿
出版型式 ,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120023 )

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