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Authors: Laurent Gomez 1 ; Marcus Wilhelm 2 ; José Márquez 3 and Patrick Duverger 4

Affiliations: 1 SAP Security Research, SAP Global Security and France ; 2 Hasso Plattner Institute, University of Potsdam and Germany ; 3 SAP Portfolio Strategy & Technology Adoption, SAP SE and Germany ; 4 Logistic & IT Services, City of Antibes Juan-les-Pins and France

ISBN: 978-989-758-378-0

Keyword(s): Distributed Systems, Neural Networks, Intellectual Property, Data Protection & Privacy, Fully Homomorphic Encryption.

Related Ontology Subjects/Areas/Topics: Data and Application Security and Privacy ; Data Protection ; Information and Systems Security ; Information Assurance ; Intellectual Property Protection ; Security in Distributed Systems

Abstract: Current developments in Enterprise Systems observe a paradigm shift, moving the needle from the backend to the edge sectors of those; by distributing data, decentralizing applications and integrating novel components seamlessly to the central systems. Distributively deployed AI capabilities will thrust this transition. Several non-functional requirements arise along with these developments, security being at the center of the discussions. Bearing those requirements in mind, hereby we propose an approach to holistically protect distributed Deep Neural Network (DNN) based/enhanced software assets, i.e. confidentiality of their input & output data streams as well as safeguarding their Intellectual Property. Making use of Fully Homomorphic Encryption (FHE), our approach enables the protection of Distributed Neural Networks, while processing encrypted data. On that respect we evaluate the feasibility of this solution on a Convolutional Neuronal Network (CNN) for image classification deploy ed on distributed infrastructures. (More)

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Paper citation in several formats:
Gomez, L.; Wilhelm, M.; Márquez, J. and Duverger, P. (2019). Security for Distributed Deep Neural Networks: Towards Data Confidentiality & Intellectual Property Protection.In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT, ISBN 978-989-758-378-0, pages 439-447. DOI: 10.5220/0007922404390447

@conference{secrypt19,
author={Laurent Gomez. and Marcus Wilhelm. and José Márquez. and Patrick Duverger.},
title={Security for Distributed Deep Neural Networks: Towards Data Confidentiality & Intellectual Property Protection},
booktitle={Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT,},
year={2019},
pages={439-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007922404390447},
isbn={978-989-758-378-0},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 2: SECRYPT,
TI - Security for Distributed Deep Neural Networks: Towards Data Confidentiality & Intellectual Property Protection
SN - 978-989-758-378-0
AU - Gomez, L.
AU - Wilhelm, M.
AU - Márquez, J.
AU - Duverger, P.
PY - 2019
SP - 439
EP - 447
DO - 10.5220/0007922404390447

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