A Random Decision Tree Framework for Privacy-Preserving Data Mining

Abstract

Distributed data is ubiquitous in modern information driven applications. With multiple sources of data, the natural challenge is to determine how to collaborate effectively across proprietary organizational boundaries while maximizing the utility of collected information. Since using only local data gives suboptimal utility, techniques for privacy-preserving collaborative knowledge discovery must be developed. Existing cryptography-based work for privacy-preserving data mining is still too slow to be effective for large scale data sets to face today's big data challenge. Previous work on random decision trees (RDT) shows that it is possible to generate equivalent and accurate models with much smaller cost. We exploit the fact that RDTs can naturally fit into a parallel and fully distributed architecture, and develop protocols to implement privacy-preserving RDTs that enable general and efficient distributed privacy-preserving knowledge discovery.