An improved algorithm for privacy-preserving data mining based on NMF

被引：0
|
作者：
Li, Guang [1 ]
Xi, Meng [1 ]
机构：
[1] The School of Electronic and Control Engineering, Chang'an University, Xi'an
来源：
Journal of Information and Computational Science | 2015年 / 12卷 / 09期
关键词：
Data Mining; Non-negative Matrix Factorization; Privacy Protection;
D O I：
10.12733/jics20106015
中图分类号：
学科分类号：
摘要：
With the development of data mining technologies, privacy protection is becoming a challenge for data mining applications in many fields. To solve this problem, many PPDM (Privacy-preserving Data Mining) methods have been proposed. One important PPDM method is based on NMF (Non-negative Matrix Factorization). This paper proposed an improved NMF-based PPDM method. Compared to the original one, this new method can keep data utility well and protect privacy better. ©, 2015, Journal of Information and Computational Science. All right reserved.
引用
收藏
页码：3423 / 3430
页数：7
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