An Enhanced Data Mining Technique for Hiding Sensitive Information

Abstract

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence
users to provide spurious information. To preserve client privacy in the data mining process, a variety of techniques based on
random perturbation of data records have been proposed recently. One known fact which is very important in data mining is
discovering the association rules from database of transactions where each transaction consists of set of items. Two important
terms support and confidence are associated with each of the association rule. Actually any rule is called as sensitive if its
disclosure risk is above a certain privacy threshold. Sometimes we do not want to disclose sensitive rules to the public because of
confidentiality purposes. There are many approaches to hide certain association rules which take the support and confidence as a
base for algorithms and many more). The proposed work has the basis of reduction of support and confidence of sensitive rules
but this work is not editing or disturbing the given database of transactions directly .The proposed algorithm uses some modified
definition of support and confidence so that it would hide any desired sensitive association rule without any side effect. Actually
the enhanced technique is using the same method (as previously used method) of getting association rules but modified definitions
of support and confidence are used.

 

 

 

Keywords: Data mining, Data hiding, Support, Confidence, and Association rules etc.