Level Up: Private Non-Interactive Decision Tree Evaluation using Levelled Homomorphic Encryption. (arXiv:2309.06496v1 [cs.CR])

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. …

aim decision encryption evaluation homomorphic encryption information intellectual property machine machine learning non privacy private property protect service service providers trees