PolyDNN: Polynomial Representation of NN for Communication-less SMPC Inference

• URL: http://arxiv.org/abs/2104.00863v1
• Date: Fri, 2 Apr 2021 02:59:37 GMT
• Title: PolyDNN: Polynomial Representation of NN for Communication-less SMPC Inference
• Authors: Philip Derbeko and Shlomi Dolev
• Abstract summary: We show a way to complete networks into a single and how to calculate 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.
• Score: 5.1779474453796865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. 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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