文章基本信息

标题：Effective than Bucketization for the Sensitive Attribute Data Publication
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作者：Dr. V.Vijayadeepa ; D.Deepika
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：12
页码：22485
DOI：10.15680/IJIRSET.2017.0612048
出版社：S&S Publications
摘要：Privacy-preserving data mining (PPDM) refers to the area of data mining that seeks to safeguardsensitive information from disclosure. There are anonymization techniques, like generalization and bucketization, havebeen designed for privacy preserving microdata publishing. But generalization loses considerable amount ofinformation, for high-dimensional data. Bucketization does not prevent membership disclosure and does not apply fordata that do not have a clear separation between quasi-identifying attributes and sensitive attributes. A new noveltechnique called slicing, which partitions the data both horizontally and vertically. Overlapping slicing, this duplicatesan attribute in more than one column. Overlap slicing preserves better utility than generalization and is more effectivethan bucketization for the sensitive attribute and used to prevent membership disclosure.
关键词：Data anonymization; overlapping slicing; Data Privacy