Hiding Sensitive Association Rules Efficiently By Introducing New Variable Hiding counter

we know that large database contain certain information that must be protected against unauthorized access. 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. In this paper we discuss confidentiality issues of a broad category of association rules. 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 ([1, 2, 6] and many more). Our approach is a modification of ISL (increase support of LHS) and DSR (decrease support of RHS) and has some modifications so that it hides any desired association rule as previous work sometimes can not. Our work has the basis of reduction of support and confidence of sensitive rules but in our work we are not editing or disturbing the given database of transactions directly(as it is generally done in previous works) rather we are performing the same task indirectly bye modifying the some new introduced terms associated with database transactions and association rules. These new terms are M-confidence (modified confidence), M-support (modified support) and Hiding counter. Our algorithm use some modified definition of support and confidence so that it would hide any desired sensitive association rule without any side effect. Actually we are using the same method (as previously used method) of getting association rules but we are modifying the definitions of support and confidence.