An Improved Approach to High Level Privacy Preserving Itemset Mining

Details An Improved Approach to High Level Privacy Preserving Itemset Mining An Improved Approach to High Level Privacy Preserving Itemset Mining

2010-01-13

arxiv.org/abs/1001.2270v1

International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 3, pp. 216-223, December 2009, USA

Computer Science

Databases

8 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS December 2009, ISSN 1947 5500,

Scientific paper

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.

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