Research Article
A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 42 - Issue 4 |
| Published: March 2012 |
| Authors: Bhavana Abad (Khivsara), Kinariwala S.A. |
10.5120/5680-7717
|
Bhavana Abad (Khivsara), Kinariwala S.A. . A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity. International Journal of Computer Applications. 42, 4 (March 2012), 13-16. DOI=10.5120/5680-7717
@article{ 10.5120/5680-7717,
author = { Bhavana Abad (Khivsara),Kinariwala S.A. },
title = { A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 42 },
number = { 4 },
pages = { 13-16 },
doi = { 10.5120/5680-7717 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Bhavana Abad (Khivsara)
%A Kinariwala S.A.
%T A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity%T
%J International Journal of Computer Applications
%V 42
%N 4
%P 13-16
%R 10.5120/5680-7717
%I Foundation of Computer Science (FCS), NY, USA
Abstract
K-anonymity is one of the easy and efficient technique to achieve privacy preserving for sensitive data in many data publishing applications. In k-anonymity techniques, all tuples of releasing database are generalized to make it anonymize which lead to reduce the data utility and more information loss of publishing table. This paper firstly proposes a Sensitivity Based Tuple Anonymity Method. In this method first we consider the sensitivity of values in sensitive attribute and then only tuples having sensitive values are generalized, and the other tuples can be directly published. Experiment results on the Adult Database show the proposed methods not only can improve the accuracy of the publishing data, but also can preserve privacy
References
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