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Reseach Article

Enhanced Batch Generation based Multilevel Trust Privacy Preserving in Data Mining

by B. Anitha, B. Hanmanthu, B. Raghu Ram
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 9
Year of Publication: 2013
Authors: B. Anitha, B. Hanmanthu, B. Raghu Ram
10.5120/14144-1907

B. Anitha, B. Hanmanthu, B. Raghu Ram . Enhanced Batch Generation based Multilevel Trust Privacy Preserving in Data Mining. International Journal of Computer Applications. 82, 9 ( November 2013), 16-22. DOI=10.5120/14144-1907

@article{ 10.5120/14144-1907,
author = { B. Anitha, B. Hanmanthu, B. Raghu Ram },
title = { Enhanced Batch Generation based Multilevel Trust Privacy Preserving in Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 9 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number9/14144-1907/ },
doi = { 10.5120/14144-1907 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:18.186191+05:30
%A B. Anitha
%A B. Hanmanthu
%A B. Raghu Ram
%T Enhanced Batch Generation based Multilevel Trust Privacy Preserving in Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 9
%P 16-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The motivation of Privacy Preserving Data Mining (PPDM) is to obtain valid data mining results without access to the original sensitive information. The different privacy preserving technique on Perturbation based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. This proposed work is based on perturbation based privacy preserving data mining. Here random perturbation approach is applied to provide privacy on the data set. Previously privacy is limited to single level trust in providing privacy to the data but now it is enhanced to multi level trust. The problem with existing multi level trust PPDM algorithms is that they fail to protect form non linear attacks. Considering that this proposed work make uses enhanced batch generation to provide privacy in the multi level trust in which data will perturb multiple times so that it can avoid non linear attacks.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Privacy Preserving Data Mining Multi Level Trust Batch generation based perturbation