Privacy Preserving Decision Tree Learning over Vertically Partitioned Data

Data mining over multiple data sources has become an important practical problem with applications in different areas. Although the data sources are willing to mine the union of their data, they donpsilat want to reveal any sensitive and private information to other sources due to competition or legal concerns. In this paper, we consider a scenario where data are vertically partitioned over more than two parties. We focus on the classification problem, and present a novel privacy preserving decision tree learning method. Theoretical analysis and experiment results show that this method can provide good capability of privacy preserving, accuracy and efficiency.