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Privacy Preservation Data Mining Using GSlicing Approach

Publication Date : 10/07/2015

Author(s) :

Mr. Ghanshyam P. Dhomse , Prof. Sonal Patil.

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

Data mining is one of the most interested research area for many people. After mining the data it is necessary to publishing the data in proper way so that to keep Privacy and data utility of micro data, because it contains the sensitive information about individual. There are various anonymization techniques are used to fulfill requirement of balancing between privacy and data utility such as Generalization, Bucketization and Slicing approach but when compare this technique it is found some drawback like in generalization it is not suitable for large size of database due there is loss of information if size of data is increased. in case bucketization there is lots of tuple having same value in same bucket due to this it cannot prevent the membership disclosure. Another problem is difficult to identify the quasi-identifying attributes and sensitive attributes because of no separation between them. Slicing is better than that of both provides the membership and attributes disclosure protection but it contains some issue like it takes more time to search the fake tuple in original data set, it displays the record as it is while publishing the data that reduces the privacy. In case of slicing, for attribute clustering correlation is applied on discrete attributes, hence equal width Discretization is applied, but this method is very time consuming because it requires lot of sorting of data. So for solving this issue a new GSlicing Approach has been proposed .it does not require sorting of data for Discretization and it is better than previous slicing technique.

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