Result Paper of Privacy Preserving Random Decision Tree over Partition Data

Information Technology

In recent years distributed data is present everywhere in current information driven approach. For the various sources of data, the inherent challenge is how to decide to merge effectively across organizational border line while maximizing the benefit of information collection. Privacy-preserving knowledge discovery techniques must be developed because local data is used suboptimal utility. Previous privacy-preserving cryptography work is too slow to be used for huge data sets to face difficulties for large data. The past work on Random Decision Trees (RDT) introduce that to possible to generate identical and accurate models with smaller cost .In this paper to utilize the fact that RDTs can particularly fit into a distributed architecture such as fully and parallel , and originate some protocols to execute RDTs that authorize distributed knowledge discovery for privacy-preserving.

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