文章基本信息

标题：A Comparative Study of Multiplicative Data Perturbation Techniques for Privacy Preserving Data Mining
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作者：Bhupendra Kumar Pandya ; Umesh kumar Singh ; Keerti Dixit 等
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2015
卷号：4
期号：4
页码：1387-1393
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Data perturbation techniques are one of the most popular models for privacy preserving data mining. It is especially useful for applications where data owners want to participate in cooperative mining but at the same time want to prevent the leakage of privacy- sensitive information in their published datasets. The goal of privacy preserving data mining is to develop data mining methods without increasing the risk of misuse of the data used to generate those methods. The topic of privacy preserving data mining has been extensively studied by the data mining community in recent years. This research paper systematically investigated different multiplicative data perturbation techniques for privacy preserving data mining. These types of perturbation distort the private data by multiplying some random noise and only the perturbed version is released for data mining analysis. We have analyzed these techniques on the basis of Utility, Privacy and accuracy and we have deduced the pros and cons of each technique.
关键词：Multiplicative Data Perturbation Techniques