文章基本信息

标题：Vector Quantization for Privacy Preserving Clustering in Data Mining
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作者：D.Aruna Kumari ; Rajasekhara Rao ; M.Suman 等
期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2012
卷号：3
期号：6
DOI：10.5121/acij.2012.3608
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Large Volumes of personal data is regularly collected from different sources and analyzed by different types of applications using data mining algorithms , sharing of these data is useful to the application users.On one hand it is an important asset to business organizations and governments for decision making at the same time analysing such data opens treats to privacy if not done properly. This paper aims to reveal the information by protecting sensitive data. We are using Vector quantization technique for preserving privacy. Quantization will be performed on training data samples it will produce transformed data set. This transformed data set does not reveal the sensitive data. And one can apply data mining algorithms on transformed data and can get accurate results by preserving privacy
关键词：Vector quantization; code book generation; privacy preserving data mining ;k-means clustering.