Achieving Privacy in Data Mining Using Normalization

G  Manikandan; N  Sairam; S  Sharmili  and S  Venkatakrishnan

doi:10.17485/ijst/2013/v6i4.16

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Achieving Privacy in Data Mining Using Normalization

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2013/v6i4.16

Year: 2013, Volume: 6, Issue: 4, Pages: 1-5

Original Article

Achieving Privacy in Data Mining Using Normalization

G. Manikandan^1*, N. Sairam², S. Sharmili³ and S. Venkatakrishnan⁴

¹Assistant Professor, School of Computing, SASTRA University, [email protected]
²Professor, School of Computing, SASTRA University, [email protected]
^{3, 4} Student, School of Computing, SASTRA University, [email protected]
[email protected]
*Author For Correspondence
G. Manikandan
Assistant Professor, School of Computing
Email:[email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

To extract the previously unknown patterns from a large data set is the ultimate goal of any data mining algorithm. Some private or confidential information may be revealed as part of data mining process. In this paper we use min-max normalization approach for preserving privacy during the mining process. We sanitize the original data using min-max normalization approach before publishing. For experimental purpose we have used k-means algorithm and from our results it is evident that our approach preserves both privacy and accuracy.
Keywords: Accuracy, Clustering, K-Means, Min-Max Normalization, Privacy