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Authors: Gamze Tillem 1 ; Beyza Bozdemir 2 and Melek Önen 2

Affiliations: 1 Delft University of Technology, Delft, The Netherlands ; 2 EURECOM, Sophia Antipolis, France

Keyword(s): Privacy, Neural Networks, Secure Two-party Computation, Homomorphic Encryption.

Abstract: The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to cloud servers which have domain-specific expertise and computational resources for the required analytics. Machine Learning as a Service (MLaaS) is one such service which provides the enterprises to perform machine learning tasks on the cloud. Despite the advantage of eliminating the need for computational resources and domain expertise, sharing sensitive data with the cloud server brings a privacy risk to the enterprises. In this paper, we propose SwaNN, a protocol to privately perform neural network predictions for MLaaS. SwaNN brings together two well-known techniques for secure computation: partially homomorphic encryption and secure two-party computation, and computes neural network predictions by switching between the two methods. The hybrid nature of SwaNN enables to maintain the accuracy of predictions and to optimize the computation time and bandwidth usage. Our experiments show that SwaNN achieves a good balance between computation and communication cost in neural network predictions compared to the state-of-the-art proposals. (More)

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Paper citation in several formats:
Tillem, G.; Bozdemir, B. and Önen, M. (2020). SwaNN: Switching among Cryptographic Tools for Privacy-preserving Neural Network Predictions. In Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT; ISBN 978-989-758-446-6; ISSN 2184-7711, SciTePress, pages 497-504. DOI: 10.5220/0009890704970504

@conference{secrypt20,
author={Gamze Tillem. and Beyza Bozdemir. and Melek Önen.},
title={SwaNN: Switching among Cryptographic Tools for Privacy-preserving Neural Network Predictions},
booktitle={Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT},
year={2020},
pages={497-504},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009890704970504},
isbn={978-989-758-446-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT
TI - SwaNN: Switching among Cryptographic Tools for Privacy-preserving Neural Network Predictions
SN - 978-989-758-446-6
IS - 2184-7711
AU - Tillem, G.
AU - Bozdemir, B.
AU - Önen, M.
PY - 2020
SP - 497
EP - 504
DO - 10.5220/0009890704970504
PB - SciTePress