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Oblivious Neural Network Computing via Homomorphic Encryption

EURASIP Journal on Information Security volume 2007, Article number: 037343 (2007) Cite this article

Abstract

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. The considered level of protection ensures that the data provided to the network and the network weights and activation functions are kept secret. Particular attention is given to prevent any disclosure of information that could bring a malevolent user to get access to the NN secrets by properly inputting fake data to any point of the proposed protocol. With respect to previous works in this field, the interaction between the user and the NN owner is kept to a minimum with no resort to multiparty computation protocols.

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Authors and Affiliations

  1. Department of Electronics and Telecommunications, University of Florence, Via S.Marta 3, Firenze, 50139, Italy

    C Orlandi & A Piva

  2. Department of Information Engineering, University of Siena, Via Roma 56, Siena, 53100, Italy

    M Barni

Authors
  1. C Orlandi
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  2. A Piva
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  3. M Barni
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Correspondence to C Orlandi.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Orlandi, C., Piva, A. & Barni, M. Oblivious Neural Network Computing via Homomorphic Encryption. EURASIP J. on Info. Security 2007, 037343 (2007). https://doi.org/10.1155/2007/37343

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  • DOI: https://doi.org/10.1155/2007/37343

Keywords

  • Neural Network
  • Data Processing
  • Activation Function
  • Data Security
  • Network Weight