  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 antimonotonic property.
