文章基本信息

标题：Data Restoration and Privacy Preserving of Data Mining Using Random Decision Tree Over Vertically Partitioned Data
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作者：K.Muthukarupaee ; Blessyselvam ; V.Ranjani 等
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2016
卷号：5
期号：5
页码：16329-16332
DOI：10.18535/Ijecs/v5i5.02
出版社：IJECS
摘要：In recent years with the development of network data collection and storage technology, the usage and sharing of large amounts ofdata has become easy process. Once the data and information is accumulated, its will become the wealth of information. Data mining,otherwise known as knowledge discovery, can extracted meaningful information from the large amounts of data because it supports people’sdecision- making. However the traditional data mining techniques and algorithms directly operated on the original data set, which will causethe leakage of privacy data, these problems challenge the traditional data mining, so privacy-preserving data mining (PPDM) it has becomeone of the most newest trends in the privacy, security and data mining research. Existing cryptography is the based work for privacy-preservingdata mining and is still too slow to be effective for the large scale. But proposed approach is based on exploit for fact that RDTs can naturallyit fit into the parallel and fully distributed architecture