An assessment of Identity Security in Data Mining
Kirubhakar Gurusamy1, Venkatesh Chakrapani2
1Kirubhakar Gurusamy, Research Scholar, Surya Engineering College, Erode.
2Venkatesh Chakrapani, Dean, Faculty of Engineering, Erode Builder Educational Trust’s Group of Institutions, Kangayam.
Manuscript received on June 05, 2013. | Revised Manuscript received on June 11, 2013. | Manuscript published on June 15, 2013. | PP: 29-31 | Volume-1 Issue-7, June 2013. | Retrieval Number: G0349061713/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Privacy preserving becomes an important issue in the development progress of data mining techniques. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. So people have become increasingly unwilling to share their data. This frequently results in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. This paper intends to reiterate several privacy preserving data mining technologies clearly and then proceeds to analyze the merits and shortcomings of these technologies..
Keywords: Privacy preserving; data mining.