Privacy-preservation association rules mining based on fuzzy correlation
Huajin Wang, Chengfu Yi · 2012
Most existing techniques work on hiding association rules in Boolean data. Based on analyzing fuzzy correlation, we have introduced a new scheme for privacy-preservation in fuzzy association rules mining, named PPM-Scheme, which is able to achieve complete hiding of sensitive rules mined in quantitative data by using improved technique in which we replace the highest value of fuzzy item with zero. Experimental results show that the proposed scheme hides more sensitive rules with minimum number of modifications and maintains quality of the released data than those previous techniques.