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Reseach Article

Efficient Encrypted Data Distribution Vertically by Generating Frequent Pattern

by Preeti Pal Singh, Anjana Verma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 9
Year of Publication: 2016
Authors: Preeti Pal Singh, Anjana Verma
10.5120/ijca2016909898

Preeti Pal Singh, Anjana Verma . Efficient Encrypted Data Distribution Vertically by Generating Frequent Pattern. International Journal of Computer Applications. 142, 9 ( May 2016), 7-10. DOI=10.5120/ijca2016909898

@article{ 10.5120/ijca2016909898,
author = { Preeti Pal Singh, Anjana Verma },
title = { Efficient Encrypted Data Distribution Vertically by Generating Frequent Pattern },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 9 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number9/24922-2016909898/ },
doi = { 10.5120/ijca2016909898 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:44:31.539467+05:30
%A Preeti Pal Singh
%A Anjana Verma
%T Efficient Encrypted Data Distribution Vertically by Generating Frequent Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 9
%P 7-10
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid increase in digital world servers security for data is highly required. So most of researcher have proposed different techniques such as data partition and modification. Here proposed work has resolve this issue of digital data security by vertical partition and AES encryption algorithm. In this work vertical patterns are generate from the database by the use of aprior algorithm of association rule mining. These patterns effectively distribute data for different sites. While ach site maintain an index table of inserted rows for proper database operations. Experiment is done on real adult dataset. Results shows that proposed work is better as compare to previous existing algorithm on different evaluation parameters.

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Index Terms

Computer Science
Information Sciences

Keywords

Conditional Functional Dependency Data Anonymization Encryption Effective Pruning