Data Mining and Information Security in Big Data

S. Sathyamoorthy¹

1. Dept. of Information Technology, Bharathiar University Atrs and Science College, Gudalur, India.

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.3 , pp.86-91, Jun-2017

Online published on Jun 30, 2017

Copyright © S. Sathyamoorthy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The growing popularity and development of data mining technologies bring serious threat to the security of individual`s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we focus on his privacy and how to protect sensitive information.

Key-Words / Index Term :
Data Mining, Sensitive Information, Privacy-Preserving Data Mining Provenance, Anonymization , Privacy Auction, Antitracking

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