A Survey on Privacy-Preserving Data Mining using Random Decision Tree

Distributed data is widely spread in advanced information driven applications. With various sources of data, main problem is to decide how to collaborate adequately crosswise over collaborate limits while maximizing the utility of gathered information. Since utilizing local data provides suboptimal utility, methods for privacy-preserving collaborative knowledge detection must be produced. Past cryptography-based work privacy-preserving data mining is still excessively slower to be powerful for huge scale data sets to handle today's huge data problem. Past work on Random Decision Trees (RDT) demonstrates that it is conceivable to produce proportionate and exact models with much more modest cost. The fact can be utilized fully that RDTs can characteristically fit into a parallel and completely distributed framework, and generate protocols to develop privacy-preserving RDTs that allow general and effective distributed privacy-preserving knowledge discovery.