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This article is part of the series Signal Processing in the Encrypted Domain.

Open Access Open Badges Research Article

Oblivious Neural Network Computing via Homomorphic Encryption

C Orlandi1*, A Piva1 and M Barni2

Author 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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EURASIP Journal on Information Security 2007, 2007:037343  doi:10.1155/2007/37343

The electronic version of this article is the complete one and can be found online at: http://jis.eurasipjournals.com/content/2007/1/037343

Received:27 March 2007
Accepted:1 June 2007
Published:24 July 2007

© 2007 Orlandi et al.

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.

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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