Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics
Lili Zhang, Wenjie Wang, Yuqing Zhang · IEEE Access · 2019
In the last decades, pervasive computing is generating a growing quantity of data. Data mining (DM) technology has become increasingly popular. However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern. Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM. In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM). We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature. Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms. Finally, we summarize some conclusions and come up with some future directions and challenges.