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Published in:   Vol. 3 Issue 2 Date of Publication:   December 2014

Multidimensional Suppression for K-Anonymity in Public Dataset Using See5


Page(s):   60-63 ISSN:   2278-2397
DOI:   10.20894/IJDMTA. Publisher:   Integrated Intelligent Research (IIR)

Data mining is a well known technique for many applications. Privacy is very important while handling public datasets. Generalization and suppression are the two methods that are widely used for annonymizing datasets. In generalization the values are replaced with less specific but semantically consistent values and in suppression technique the value is never released. So generalization is applied for many areas since suppression may reduce the accuracy of results if not properly used. Though generalization requires manually generated domain hierarchy taxonomy for every quasi identifier in the dataset on which K-Anonymity is performed. . K-Anonymity is one of the best methods for deidentification of large public datasets. Previous K-anonymity algorithms such as kACTUS that using classifiers C4.5 for building their decision tree. In kACTUS(K-Anonymity of Classification Trees Using Suppression) efficient multidimensional suppression is performed without need for manually produced domain hierarchy trees using C4.5 algorithm In this paper better classifier such as see5 algorithm is used with kACTUS that will produce better results than previous one.