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	Privacy preserving classification based on randomization and reconstruction
	Zhang, Peng ; Tong, Yunhai ; Tang, Shiwei ; Yang, Dongqing
	2007
关键词	data mining privacy preservation classification Naive Bayes data randomization distribution reconstruction
英文摘要	Privacy preserving classification is to develop a classifier without precise access to the original data. In order to improve the applicability with higher privacy and better accuracy, we present a novel Privacy Preserving Naive Bayes (PPNB) classification method that consists of two steps: first, the original data set is distorted by a new. randomization approach; second, a naive Bayes classifier is implemented on the distorted data set to predict the class labels for unknown samples. Besides being analyzed in applicability, privacy, accuracy, and efficiency, the effectiveness of our PPNB classification method is also validated by the experiments.; Mathematics, Applied; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/293263]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Peng,Tong, Yunhai,Tang, Shiwei,et al. Privacy preserving classification based on randomization and reconstruction. 2007-01-01.

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