Privacy Preserving Data Mining Using Random Decision Tree Over Partition Data: Survey

The development of data mining with protection and utility can manage distributed efficiently. This paper revisits the concepts techniques privacy-preserving Random Decision Tree (RDT). In existing systems, cryptography-based are effective at managing information. Privacy-preserving RDT handles information gives better precision while preserving reducing calculation time. deals this headway in technology utilizing emphasized approach RDT. preferable productivity privacy than cryptographic technique. Various tasks utilize RDT, like classification, relapse, ranking, different classifications. utilizes both randomization method, giving for some decision tree-based learning tasks; is an technique Thus, horizontal partitioning dataset, parties gather various entities but have all attributes. On other hand, associations may about a similar set people. vertically partitioned data, same collection items. these cases, vertical datasets somewhat inaccurate.