The structure and weights of Deep Neural Networks (DNN) typically encode and
contain very valuable information about the dataset that was used to train the
One way to protect this information when DNN is published is to perform an
interference of the network using secure multi-party computations (MPC).
In this paper, we suggest a translation of deep neural networks to
polynomials, which are easier to calculate efficiently with MPC techniques.
We show a way to translate complete networks into a single polynomial and how
to calculate the polynomial with an efficient and information-secure MPC
The calculation is done without intermediate communication between the
participating parties, which is beneficial in several cases, as explained in