A Review on Privacy-Preserving Data Mining

被引：15
|
作者：
Li, Xueyun [1 ]
Yan, Zheng [2 ,3 ]
Zhang, Peng [4 ]
机构：
[1] Aalto Univ, Sch Elect Engn, Dept Commun & Networking, Espoo, Finland
[2] Xidian Univ, State Key Lab ISN, Xian, Shaanxi, Peoples R China
[3] Aalto Univ, Dept Commun & Networking, Espoo, Finland
[4] Xian Univ Posts & Telecommun, Inst Mobile Internet, Xian, Shaanxi, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) | 2014年
关键词：
Privacy preserving; data mining; data perturbation; k-anonymity; DATA PERTURBATION; TRUST;
D O I：
10.1109/CIT.2014.135
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Data mining has been widely studied and applied into many fields such as Internet of Things (IoT) and business development. However, data mining techniques also occur serious challenges due to increased sensitive information disclosure and privacy violation. Privacy-Preserving Data Mining (PPDM), as an important branch of data mining and an interesting topic in privacy preservation, has gained special attention in recent years. In addition to extracting useful information and revealing patterns from large amounts of data, PPDM also protects private and sensitive data from disclosure without the permission of data owners or providers. This paper reviews main PPDM techniques based on a PPDM framework. We compare the advantages and disadvantages of different PPDM techniques and discuss open issues and future research trends in PPDM.
引用
收藏
页码：769 / 774
页数：6
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