1. Secure exchange of information by synchronization of neural networks Authors: Ido Kanter, Wolfgang Kinzel and Eran Kanter From: Europhys. Lett. 2002 Presented by: 魏聲尊

2. Outline Introduction Method Exam Conclusion

3. Introduction The ability to build a secure channel is one of the most challenging fields of research in modern communication. –In private-key system: need a hidden communication –In public-key system: number theory In this report they suggest a novel cryptosystem, which is base on a learning process of neural network

4. Method(1/4) X: input unit takes binary values, (public) Y : the K binary hidden unit are denoted by W: the weight from the jth input to the kth hidden unit is denoted by w kj, bounded by L (secret) O: the output bit O is the product of the state of the hidden unit (1~k) 1~N x 11 x 21

5. Method(2/4) First, given common input vector x kj for both sender and recipient Second, calculate the hidden unit y and output O when y S =1,y R =-1

6. Method(3/4) Third, if O S O R < 0 update according to Hebbian learning rule Finally, there are two ways to encrypt the message –First, use a conventional encryption algorithm – BBS –Second, use the PM itself for stream cipher by multiplying its output bit

7. Method(4/4) N=2, K = 2, L = 2, input x = -1-11-1, w s =2,-1,1,1 w R =2,1,2,-1 1 2 1 1 Sender 1 2 2 1 recipient 1

8. Exam

11. Conclusion The phenomenon describe here suggests that synchronization can be used by biological neuronal networks to exchange secure information between different parts of an organism