Abstract-Privacy preserving association rule mining has triggered the development of many privacy-preserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving. This paper proposes a new transaction randomization method which is a combination of the fake transaction randomization method and a new per-transaction randomization method. This method distorts the items within each transaction and ensures a higher level of data privacy in comparison to the previous approaches. The pertransaction randomization method involves a randomization function to replace the item by a random number guarantying privacy within the transaction also. A tool has also been developed to implement the proposed approach to mine frequent itemsets and association rules from the data guaranteeing the anti-monotonic property.
Another similar method is given by REF where transaction randomization method is a combination of the fake transaction randomization method and a new per-transaction randomization method.
An Improved Approach to High Level Privacy Preserving Itemset Mining
International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 3, pp. 216-223, December 2009, USA
Computer Science