Architecture for Preserving Privacy During Data Mining by Hybridization of Partitioning on Medical Data

Data mining technique, classification and prediction has improved and used in medical domain to helping medical practitioner in making their decisions. As medical data is highly sensitive to personal information of human being, so it is desired to keep private. There are many approaches for classification which have been adapted for privacy preserving in medical data which are based on data separation technique for privacy preserving. There are two scenarios, one is centralized and other is distributed data where several approaches have been developed. In this paper, we propose architecture for privacy preserving in data mining by combining horizontal data distribution and vertical data distribution for breast cancer data set.