@inproceedings{7a0ee0c689054ac2836c1a7afb0fe108,
title = "PolyDNN Polynomial Representation of NN for Communication-Less SMPC Inference",
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.",
keywords = "DNN, Data publishing, Data sharing, Privacy",
author = "Philip Derbeko and Shlomi Dolev",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 ; Conference date: 08-07-2021 Through 09-07-2021",
year = "2021",
month = jan,
day = "1",
doi = "https://doi.org/10.1007/978-3-030-78086-9_24",
language = "الإنجليزيّة",
isbn = "9783030780852",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "317--324",
editor = "Shlomi Dolev and Oded Margalit and Benny Pinkas and Alexander Schwarzmann",
booktitle = "Cyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings",
address = "ألمانيا",
}