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Reseach Article

Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR

Published on August 2012 by G. Sudha Sadasivam, S. Sangeetha, K. Sathyapriya
Information Processing and Remote Computing
Foundation of Computer Science USA
IPRC - Number 1
August 2012
Authors: G. Sudha Sadasivam, S. Sangeetha, K. Sathyapriya

G. Sudha Sadasivam, S. Sangeetha, K. Sathyapriya . Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR. Information Processing and Remote Computing. IPRC, 1 (August 2012), 19-30.

author = { G. Sudha Sadasivam, S. Sangeetha, K. Sathyapriya },
title = { Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR },
journal = { Information Processing and Remote Computing },
issue_date = { August 2012 },
volume = { IPRC },
number = { 1 },
month = { August },
year = { 2012 },
issn = 0975-8887,
pages = { 19-30 },
numpages = 12,
url = { /specialissues/iprc/number1/8000-1008/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Special Issue Article
%1 Information Processing and Remote Computing
%A G. Sudha Sadasivam
%A S. Sangeetha
%A K. Sathyapriya
%T Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR
%J Information Processing and Remote Computing
%@ 0975-8887
%N 1
%P 19-30
%D 2012
%I International Journal of Computer Applications

Data mining aims at extracting hidden information from data. Data mining poses a threat to information privacy. Privacy preserving data mining hides the sensitive rules and prevents the data from being disclosed to the public. Attribute reduction techniques reduce the dimensionality of dataset. Rough sets are used for attribute reduction to yield reduced sets. An attribute reduct is a subset of attributes formed using rough sets. This paper proposes two approaches to hide sensitive fuzzy association rules namely, decreasing support value of item in RHS of association rule and Particle Swarm Optimization (PSO). The proposed approach is implemented using map reduce paradigm. Experimental results demonstrate the performance of the proposed approach.

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Index Terms

Computer Science
Information Sciences


Rough Sets Attribute Reduction Map Reduce Discernibility Matrix Pso Privacy Preserving Data Mining Fuzzification Dsr Quantitative Association Rule Lost Rule Ghost Rule