  Tree-based models are among the most efficient machine learning techniques
for data mining nowadays due to their accuracy, interpretability, and
simplicity. The recent orthogonal needs for more data and privacy protection
call for collaborative privacy-preserving solutions. In this work, we survey
the literature on distributed and privacy-preserving training of tree-based
models and we systematize its knowledge based on four axes: the learning
algorithm, the collaborative model, the protection mechanism, and the threat
model. We use this to identify the strengths and limitations of these works and
provide for the first time a framework analyzing the information leakage
occurring in distributed tree-based model learning.
