A Novel Data Mining Approach for Information Hiding

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 ([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 .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.