Novel Algorithms for Privacy Preserving Utility Mining

Privacy preserving data mining (PPDM) has become a popular topic in the research community. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries can not mine them from the modified database. In addition, we minimize the impact on the sanitized database in the process of hiding sensitive itemsets. The experimental results show that HHUIF achieves the lower miss costs than MSICF does on two synthetic datasets. On the other hand, MSICF generally has the lower difference between the original and sanitized databases than HHUIF does.