Classification and evaluation of Privacy Preserving Data Mining: A review
Aobakwe Senosi, George Sibiya · 2017
Due to advances in communication and storage technologies, there is recent exponential increase in data generated and collected. Data mining has become a necessity to enable easier and efficient means of data processing. Data mining is a process of harvesting previously unknown information from existing data and utilizing such insight for business decision making. However, in sectors such as health, exposure of sensitive information is not a trivial issue, despite benefits of data mining. Hence, researchers have proposed various approaches on seeking valuable insight from data but being cautious to protect private data and information. The purpose of this paper is to provide a conceptual review of approaches by researchers in Privacy-Preserving Data Mining (PPDM). This paper further presents a classification of PPDM techniques and provide a clear description of distinctions of one class of techniques from the other, guided by discussions in literature. Lastly, an evaluation criteria which can be used as a building block towards standardizing evaluation criteria for PPDM techniques is proposed.