BloomDT - An improved privacy-preserving decision tree inference scheme

Abstract

Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model hosting. However, model providers may be forced to disclose private model details when hosting their models in the cloud. Due to the time and monetary investments associated with model training, model providers may be reluctant to host their models in the cloud due to these privacy concerns. Furthermore, clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud servers. In this paper, we propose BloomDT, a privacy-preserving scheme for decision tree inference, which uses Bloom filters to hide the original decision tree's structure, the threshold values of each node, and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers collude. Our scheme's security and performance are verified through rigorous testing and analysis.