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Authors: Beyza Bozdemir ; Orhan Ermis and Melek Önen

Affiliation: EURECOM, Sophia Antipolis, France

Keyword(s): Privacy, Neural Networks, Homomorphic Proxy Re-encryption.

Abstract: In this work, we propose ProteiNN, a privacy-preserving neural network classification solution in a one-to-many scenario whereby one model provider outsources a machine learning model to the cloud server for its many different customers, and wishes to keep the model confidential while controlling its use. On the other hand, these customers take advantage of this machine learning model without revealing their sensitive inputs and the corresponding results. The solution employs homomorphic proxy re-encryption and a simple additive encryption to ensure the privacy of customers’ inputs and results against the model provider and the cloud server, and to give the control on the privacy and use of the model to the model provider. A detailed security analysis considering potential collusions among different players is provided.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bozdemir, B.; Ermis, O. and Önen, M. (2020). ProteiNN: Privacy-preserving One-to-Many Neural Network Classifications. 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 397-404. DOI: 10.5220/0009829603970404

@conference{secrypt20,
author={Beyza Bozdemir. and Orhan Ermis. and Melek Önen.},
title={ProteiNN: Privacy-preserving One-to-Many Neural Network Classifications},
booktitle={Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT},
year={2020},
pages={397-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009829603970404},
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 - ProteiNN: Privacy-preserving One-to-Many Neural Network Classifications
SN - 978-989-758-446-6
IS - 2184-7711
AU - Bozdemir, B.
AU - Ermis, O.
AU - Önen, M.
PY - 2020
SP - 397
EP - 404
DO - 10.5220/0009829603970404
PB - SciTePress