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A Survey: Privacy Preservation Techniques in Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 119 - Number 4 |
| Year of Publication: 2015 |
| Authors: Hina Vaghashia, Amit Ganatra |
10.5120/21056-3704
|
Hina Vaghashia, Amit Ganatra . A Survey: Privacy Preservation Techniques in Data Mining. International Journal of Computer Applications. 119, 4 ( June 2015), 20-26. DOI=10.5120/21056-3704
Abstract
Data mining tools aims to find useful patterns from large amount of data. These patterns represent information and are conveyed in decision trees, clusters or association rules. The knowledge discovered by various data mining techniques may contain private information about people or business. Preservation of privacy is a significant aspect of data mining and thus study of achieving some data mining goals without losing the privacy of the individuals' . The analysis of privacy preserving data mining (PPDM) algorithms should consider the effects of these algorithms in mining the results as well as in preserving privacy. Within the constraints of privacy, several methods have been proposed but still this branch of research is in its formative years . The success of privacy preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms etc. However no privacy preserving algorithm exists that outperforms all others on all possible criteria. Rather, an algorithm may perform better than another on one specific criterion. So, the aim of this paper is to present current scenario of privacy preserving data mining framework and techniques.
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