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k-Support Anonymity Based on Pseudo Taxonomy for Outsourcing of Frequent Itemset Mining

k-Support Anonymity Based on Pseudo Taxonomy for Outsourcing of Frequent Itemset Mining

This video was recorded at 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington 2010. For any outsourcing service, privacy is a major concern. This paper focuses on outsourcing frequent itemset mining and examines the issue on how to protect privacy against the case where the attackers have precise knowledge on the supports of some items. We propose a new approach referred to as k-support anonymity to protect each sensitive item with k-1 other items of similar support. To achieve k-support anonymity, we introduce a pseudo taxonomy tree and have the third party mine the generalized frequent itemsets under the corresponding generalized association rules instead of association rules. The pseudo taxonomy is a construct to facilitate hiding of the original items, where each original item can map to either a leaf node or an internal node in the taxonomy tree. The rationale for this approach is that with a taxonomy tree, the k nodes to satisfy the k-support anonymity may be any k nodes in the taxonomy tree with the appropriate supports. So this approach can provide more candidates for k-support anonymity with limited fake items as only the leaf nodes, not the internal nodes, of the taxonomy tree need to appear in the transactions. Otherwise for the association rule mining, the k nodes to satisfy the k-support anonymity have to correspond to the leaf nodes in the taxonomy tree. This is far more restricted. The challenge is thus on how to generate the pseudo taxonomy tree to facilitate k-support anonymity and to ensure the conservation of original frequent itemsets. The experimental results showed that our methods of k-support anonymity can achieve very good privacy protection with moderate storage overhead.

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