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2019 | OriginalPaper | Buchkapitel

Security for Distributed Machine Learning Based Software

verfasst von : Laurent Gomez, Alberto Ibarrondo, Marcus Wilhelm, José Márquez, Patrick Duverger

Erschienen in: E-Business and Telecommunications

Verlag: Springer International Publishing

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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 deployed on distributed infrastructures.

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Titel: Security for Distributed Machine Learning Based Software
verfasst von: Laurent Gomez
Alberto Ibarrondo
Marcus Wilhelm
José Márquez
Patrick Duverger
Verlag: Springer International Publishing
Buch: E-Business and Telecommunications
Print ISBN: 978-3-030-34865-6

Electronic ISBN: 978-3-030-34866-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-34866-3_6

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