  Although machine learning (ML) is widely used for predictive tasks, there are
important scenarios in which ML cannot be used or at least cannot achieve its
full potential. A major barrier to adoption is the sensitive nature of
predictive queries. Individual users may lack sufficiently rich datasets to
train accurate models locally but also be unwilling to send sensitive queries
to commercial services that vend such models. One central goal of
privacy-preserving machine learning (PPML) is to enable users to submit
encrypted queries to a remote ML service, receive encrypted results, and
decrypt them locally. We aim at developing practical solutions for real-world
privacy-preserving ML inference problems. In this paper, we propose a
privacy-preserving XGBoost prediction algorithm, which we have implemented and
evaluated empirically on AWS SageMaker. Experimental results indicate that our
algorithm is efficient enough to be used in real ML production environments.
