Level Up: Private Non-Interactive Decision Tree Evaluation using Levelled Homomorphic Encryption
Rasoul Akhavan Mahdavi, Haoyan Ni, Dimitry Linkov, Florian Kerschbaum · 2023
As machine learning as a service continues gaining popularity, concerns about privacy and intellectual property arise. Users often hesitate to disclose their private information to obtain a service, while service providers aim to protect their proprietary models. Decision trees, a widely used machine learning model, are favoured for their simplicity, interpretability, and ease of training. In this context, Private Decision Tree Evaluation (PDTE) enables a server holding a private decision tree to provide predictions based on a client's private attributes. The protocol is such that the server learns nothing about the client's private attributes. Similarly, the client learns nothing about the server's model besides the prediction and some hyperparameters.