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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.