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
network.

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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
algorithm.

The calculation is done without intermediate communication between the
participating parties, which is beneficial in several cases, as explained in
the paper.

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