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A MODEL FOR PRESERVING PRIVACY OF SENSITIVE CATEGORICAL DATA
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Author(s):
SHALINI LAMBA , S. QAMAR ABBAS
Vol - 5, Issue- 10 ,
Page(s) : 13 - 25
(2014 )
DOI : https://doi.org/10.32804/IRJMST
Abstract
Abstract- The aim of data mining is attaining fresh and precious knowledge from data. In many cases, the extracted knowledge is highly private and it needs sanitization before giving to data mining researchers and the public in order to address privacy concerns. In this paper we presented a model using privacy preserving technique that adds noise to each and every attribute, both numerical and categorical, of a data set. We added noise in such a way so that a high data quality is preserved in the perturbed data set. Therefore, the perturbed data set can be used for classification, prediction and correlation analyses. More-over, since we add a little amount of noise the perturbed data set can also be used for many other data analyses. Since noise is added to all attributes, it makes record re-identification determining the confidential class values difficult.
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