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Parallel Architectures, Algorithms and Programming, International Symposium on (2011)
Tianjin, China
Dec. 9, 2011 to Dec. 11, 2011
ISBN: 978-0-7695-4575-2
pp: 115-118
This paper addressed the insecurity and the inefficiency of privacy preserving association rule mining in vertically partitioned data. We presented a privacy preserving maximal frequent itemsets mining algorithm in vertically partitioned data. The algorithm adopted a more secure vector dot protocol which used an inverse matrix to hide the original input vector, and without any site revealing privacy vector. The mining strategy was based on depth-first search for the maximal frequent itemsets. Performance analysis and experimental analysis showed that the algorithm possessed higher security and efficiency.
Maximal Frequent Itemsets Mining, Privacy Preserving Data Mining, Privacy Preserving association rule mining, Vertically Partitioned Data

X. Zhang, J. Su, Y. Miao and K. Wu, "An Efficient Algorithm for Privacy Preserving Maximal Frequent Itemsets Mining," Parallel Architectures, Algorithms and Programming, International Symposium on(PAAP), Tianjin, China, 2011, pp. 115-118.
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