Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Using echo state networks for cryptography

 
: Ramamurthy, Rajkumar; Bauckhage, Christian; Buza, Krisztian; Wrobel, Stefan

:

Lintas, Alessandra:
Artificial neural networks and machine learning - ICANN 2017. Pt.2 : 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017; Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10614)
ISBN: 978-3-319-68611-0 (Print)
ISBN: 978-3-319-68612-7 (Online)
S.663-671
International Conference on Artificial Neural Networks (ICANN) <26, 2017, Alghero>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data. We make use of this property to realize a novel neural cryptography scheme. The key idea is to assume that Alice and Bob share a copy of an echo state network. If Alice trains her copy to memorize a message, she can communicate the trained part of the network to Bob who plugs it into his copy to regenerate the message. Considering a byte-level representation of in- and output, the technique applies to arbitrary types of data (texts, images, audio files, etc.) and practical experiments reveal it to satisfy the fundamental cryptographic properties of diffusion and confusion.

: http://publica.fraunhofer.de/dokumente/N-506510.html