Quality Measurements for Association Rules Hiding

Data mining technologies are widely used and help people to make good decision and good money since appeared. While at the same time, the side effects incurred are concerned by data holders and researchers. Some information extracted from mining can be considered sensitive and needed to be hidden for safe data sharing. A lot of approaches of hiding have been proposed. There is still a lot of room to improve the efficiency and quality. The work focuses on the quality measurements of hiding approaches to guide the sensitive association rule hiding process. Several crucial quality measurements are defined. The requirements of sensitive association rule hiding are described. The transformation strategies of database are proposed. Examples are illustrated and quality measurements are compared.