  Decision forests are classical models to efficiently make decision on complex
inputs with multiple features. While the global structure of the trees or
forests is public, sensitive information have to be protected during the
evaluation of some client inputs with respect to some server model. Indeed, the
comparison thresholds on the server side may have economical value while the
client inputs might be critical personal data. In addition, soundness is also
important for the receiver. In our case, we will consider the server to be
interested in the outcome of the model evaluation so that the client should not
be able to bias it. In this paper, we propose a new offline/online protocol
between a client and a server with a constant number of rounds in the online
phase, with both privacy and soundness against malicious clients. CCS Concepts:
$\bullet$ Security and Privacy $\rightarrow$ Cryptography.
