An approach of private classification on vertically partitioned data

Classification is one of the most ubiquitous data mining problems found in real life. Decision tree classification is one of the best-known solution approaches. This paper describes the construction of a decision tree classifier on vertically partitioned data owned by different owners, by concealing the data held by the parties. Our protocol uses an efficient splitting strategy as well as a semi-trusted third party to efficiently build a binary decision tree model. The third party uses a commodity server where the different owners send request and receive commodities (data) from the server, where the commodities are independent of the parties involved in classification. Commodity server assists the parties to conduct the computation for decision tree construction. The security of our classification method is based on scalar product protocol. The goal of secure protocols is to provide privacy preservation, without finding a third party that everyone trusts.