摘要：Summary In recent decades, high-utility itemset mining (HUIM) has emerging a critical research topic since the quantity and profit factors are both concerned to mine the high-utility itemsets (HUIs). Generally, data mining is commonly used to discover interesting and useful knowledge from massive data. It may, however, lead to privacy threats if private or secure information (e.g., HUIs) are published in the public place or misused. In this paper, we focus on the issues of HUIM and privacy-preserving utility mining (PPUM), and present two evolutionary algorithms to respectively mine HUIs and hide the sensitive high-utility itemsets in PPUM. Extensive experiments showed that the two proposed models for the applications of HUIM and PPUM can not only generate the high quality profitable itemsets according to the user-specified minimum utility threshold, but also enable the capability of privacy preserving for private or secure information (e.g., HUIs) in real-word applications.
