A Review on Privacy Preserving Data Mining

被引：0
作者：
Shanthi, A. S. [1 ]
Karthikeyan, M. [1 ]
机构：
[1] Tamilnadu Coll Engn, Coimbatore, Tamil Nadu, India
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC) | 2012年
关键词：
Anonymization; Data mining; Privacy preserving; Perturbed data; Randomization; K-ANONYMITY;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The information is rich: but the knowledge is poor. To gain a better knowledge from available information, number of techniques and methods has been developed in the area of data mining so far. On the other hand privacy is one of the most important properties of information that any system should satisfy. The secrecy of the information must be maintained while sharing the information among different un-trusted parties. Thus privacy plays a major role and also an important issue in most of the data mining applications. Best suited Privacy Preserving Data Mining (PPDM) algorithm for different levels of mining which minimizes the information loss and improves accuracy of the mined data are identified based on this survey.
引用
收藏
页码：438 / 441
页数：4
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