Authors:
Niansheng Liu
1
and
Donghui Guo
2
Affiliations:
1
College of Computer Engineering, Jimei University, China
;
2
Xiamen University, China
Keyword(s):
Neural networks, Public-key cryptosystem, Chaotic attractor, Matrix decomposition.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Formal Methods
;
Information Systems Analysis and Specification
;
Methodologies and Technologies
;
Operational Research
;
Security
;
Simulation and Modeling
Abstract:
A new public-key Encryption scheme based on chaotic attractors of neural networks is described in the paper. There is a one-way function relationship between the chaotic attractors and their initial states in an Overstoraged Hopfield Neural Networks (OHNN), and each attractor and its corresponding domain of attraction are changed with permutation operations on the neural synaptic matrix. If the neural synaptic matrix is changed by commutative random permutation matrix, we propose a new cryptography technique according to Diffie-Hellman public-key cryptosystem. By keeping the random permutation operation of the neural synaptic matrix as the secret key, and the neural synaptic matrix after permutation as public-key, we introduce a new encryption scheme for a public-key cryptosystem. Security of the new scheme is discussed.