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http://dx.doi.org/10.9717/kmms.2017.20.11.1785

Development of a Privacy-Preserving Big Data Publishing System in Hadoop Distributed Computing Environments  

Kim, Dae-Ho (Dept. of Computer Science, Sangmyung University)
Kim, Jong Wook (Dept. of Computer Science, Sangmyung University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.11, 2017 , pp. 1785-1792 More about this Journal
Abstract
Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.
Keywords
Privacy-Preserving Data Publishing; Hadoop; Distributed Computing; k-Anonymity;
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Times Cited By KSCI : 2  (Citation Analysis)
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