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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sung-Hwan Kim Nam-Uk Kim Tai-Myoung Chung |
| Copyright Year | 2013 |
| Description | Author affiliation: Sch. of Inf. Commun. Eng., Sungkyunkwan Univ. Suwon, Suwon, South Korea (Sung-Hwan Kim; Nam-Uk Kim; Tai-Myoung Chung) |
| Abstract | There has been an increasing interest in big data and big data security with the development of network technology and cloud computing. However, big data is not an entirely new technology but an extension of data mining. In this paper, we describe the background of big data, data mining and big data features, and propose attribute selection methodology for protecting the value of big data. Extracting valuable information is the main goal of analyzing big data which need to be protected. Therefore, relevance between attributes of a dataset is a very important element for big data analysis. We focus on two things. Firstly, attribute relevance in big data is a key element for extracting information. In this perspective, we studied on how to secure a big data through protecting valuable information inside. Secondly, it is impossible to protect all big data and its attributes. We consider big data as a single object which has its own attributes. We assume that a attribute which have a higher relevance is more important than other attributes. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 422776 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479928453 |
| DOI | 10.1109/ICITCS.2013.6717808 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-16 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Databases Data handling Information management Data mining Security Data storage systems |
| Content Type | Text |
| Resource Type | Article |
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