Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR
Sudha G Sadasivam, S Sangeetha and K Sathyapriya. Article: Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR. IJCA Special Issue on Information Processing and Remote Computing IPRC(1):19-30, August 2012. Full text available. BibTeX
@article{key:article,
author = {G. Sudha Sadasivam and S. Sangeetha and K. Sathyapriya},
title = {Article: Privacy Preservation with Attribute Reduction in Quantitative Association Rules using PSO and DSR},
journal = {IJCA Special Issue on Information Processing and Remote Computing},
year = {2012},
volume = {IPRC},
number = {1},
pages = {19-30},
month = {August},
note = {Full text available}
}
Abstract
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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