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Security for Distributed Machine Learning Based Software

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1118)

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.

Keywords

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Notes

  1. 1.

    https://www.nvidia.com/en-us/data-center/dgx-1/.

  2. 2.

    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems-dev-kits-modules/.

  3. 3.

    https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py.

  4. 4.

    https://keras.io.

  5. 5.

    https://www.cs.toronto.edu/~kriz/cifar.html.

  6. 6.

    http://yann.lecun.com/exdb/mnist/.

  7. 7.

    https://cython.org/.

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Gomez, L., Ibarrondo, A., Wilhelm, M., Márquez, J., Duverger, P. (2019). Security for Distributed Machine Learning Based Software. In: Obaidat, M. (eds) E-Business and Telecommunications. ICETE 2018. Communications in Computer and Information Science, vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-030-34866-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-34866-3_6

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