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Dr. Andreas Ruttor

Lupe
Lupe

Scientific Assistant
Office: MAR 4.019
Phone: +49 30 314-23938
E-Mail: andreas.ruttor <AT> tu-berlin.de


Consulting hour according to agreement.

Curriculum vitae

Andreas Ruttor
Born 1977

Academic Career
Period
Occupation
since 2007
Post-Doc Researcher at TU Berlin, Artificial Intelligence Group

2007
Ph. D. in Physics, Universität Würzburg
2003-2007
Research Assistant at Universität Würzburg, Statistical Physics Group

2003
Master in Physics, Universität Würzburg

Research Fields

  • Stochastic dynamical systems (exact and approximate inference, model selection)
  • Statistical learning theory (Gaussian processes, neural networks)
  • Statistical physics of complex systems
  • Applications: Systems biology, data analysis, cryptography

Publications

Dynamics of neural cryptography
Citation key Ruttor:2007:DNC
Author Andreas Ruttor and Ido Kanter and Wolfgang Kinzel
Pages 056104
Year 2007
DOI 10.1103/PhysRevE.75.056104
Journal Phys. Rev. E
Volume 75
Number 5
Abstract Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.
Link to publication Link to original publication Download Bibtex entry

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Postal Address

TU Berlin
Fakultät IV
Elektrotechnik und Informatik
sec. MAR 4-2
Marchstrasse 23
D-10587 Berlin