This paper studies the privacy-preserving association rule mining algorithm for horizontally distributed data sets. In view of the shortcomings of existing algorithms that require multiple scans of the data set, a privacy-preserving mining algorithm based on distributed FP-tree is proposed, which only requires two scans of the data set. This algorithm can effectively reduce the amount of communication and protect the original data while ensuring accuracy. Keywords: privacy protection; distributed association rule mining; frequent itemsets; multi-party secure computing
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