Quantitative Association Rules Mining Methods with Privacy-preserving

Considering the different size of quantitative attribute values and categorical attribute values in databases, we present two quantitative association rules mining methods with privacy-preserving respectively, one bases on Boolean association rules, which is suitable for the smaller size of quantitative attribute values and categorical attribute values in databases; the other one bases on partially transforming measures, which is suitable for the larger ones. To each approach, the privacy and accuracy are analyzed, and the correctness and feasibility are proven by experiments.