Privacy-Preserving Delegation of Decision Tree Classification

In the era of cloud computing with security, how to outsource a trained model to the server but preserve the model privacy is a significant problem for decision tree classification (DTC). In this paper, we aim for constructing an interactive system to realize privacy-preserving delegation for DTC. We focus on a basic structure of DTC, and believe that it can be generalization. The main technique to preserve privacy is to build secret sharing-based protocols for the model owner, user, and server. However, to overcome communication issues between the model owner and user, we achieve compression by using pseudorandom generators.