HHUIF and MSICF: Novel algorithms for privacy preserving utility mining

摘要

Privacy preserving data mining (PPDM) is 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 cannot mine them from the modified database. The work also minimizes the impact on the sanitized database of hiding sensitive itemsets. The experimental results show that HHUIF achieves lower miss costs than MSICF on two synthetic datasets. On the other hand, MSICF generally has a lower difference ratio than HHUIF between original and sanitized databases.