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EURASIP Journal on Information Security
EURASIP Journal on Information Security Cover Image
  • Research Article
  • Open Access

Oblivious Neural Network Computing via Homomorphic Encryption

EURASIP Journal on Information Security20072007:037343

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

©  C. Orlandi et al. 2007

  • Received: 27 March 2007
  • Accepted: 1 June 2007
  • Published:

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.

Keywords

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

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Authors’ Affiliations

(1)
Department of Electronics and Telecommunications, University of Florence, Via S.Marta 3, Firenze, 50139, Italy
(2)
Department of Information Engineering, University of Siena, Via Roma 56, Siena, 53100, Italy

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Copyright

© C. Orlandi et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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