文章基本信息

标题：A Literature analysis on Privacy Preserving Data Mining
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作者：Tamanna Kachwala ; Dr. L. K. Sharma
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：4
DOI：10.15680/ijircce.2015.0304025
出版社：S&S Publications
摘要：Privacy Preserving Data Mining (PPDM) is a research area concerned with the privacy driven frompersonally identifiable information when considered for data mining. Therefore, PPDM has become an increasinglyimportant field of research. PPDM is a novel research direction in data mining. A number of methods and techniqueshave been developed for privacy preserving data mining. This paper provides a complete review on PPDM anddifferent techniques such as data partition, data modification, data restriction technique which could be used to preventthe data access from unauthorized users. Privacy preserving data mining has become increasingly popular because itallows sharing of privacy Sensitive data for analysis purposes. Several data mining algorithms, incorporating privacypreserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data,while hide sensitive data or information from disclosure or inference. We provide a review of the state-of-the-artmethods for privacy and analyze the representative technique for privacy preserving data mining.
关键词：Data Mining; Privacy Preserving; Knowledge; Protection; data modification; data restriction