Privacy Preserving Distributed Association Rule Hiding Using Concept Hierarchy
Abstract Data Mining enables important knowledge to be extracted from the data. This has made data mining an important and useful emerging trend It may further be possible that data is distributed among various parties. This raises the issue of privacy. There are many data mining techniques. Association rule mining is one data mining technique and is receiving much attention from the researchers so as to enable them to find existing correlations between data items. In a distributed environment it may be important to find global association rules. The global rules has to be calculated securely without disclosing the individual information of one party to other parties. This paper uses secure association rule mining over horizontally and vertically distributed data. Further, the global rules calculated may be sensitive to some individual parties and there arises the need for association rule hiding. A method of concept hierarchy is used to hide the sensitive association rules.