Abstract
Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.
Highlights
Protocols based on public key cryptosystems have been widely used in key exchange (e.g., Diffie–Hellman [1] and RSA [2]) and the shared secret keys can be used in many applications, such as digital certificate, digital signature, and embedded system [2,3,4]. ese key exchange protocols are fundamentally based on algebraic number theory [5]
(3) eoretical proof of security and efficiency: we first show that the synchronization time of the Vector-Valued Tree Parity Machine (VVTPM) has the same order as the original Tree Parity Machine (TPM) model, which means that efficiency can be preserved. en, we prove that, with the same synaptic depth, the security of the VVTPM can be increased according to the number of vectors
We proposed a novel architecture of neural cryptography, called Vector-Valued Tree Parity Machine (VVTPM), which can be applied to generate a flexible length of secret key
Summary
Protocols based on public key cryptosystems have been widely used in key exchange (e.g., Diffie–Hellman [1] and RSA [2]) and the shared secret keys can be used in many applications, such as digital certificate, digital signature, and embedded system [2,3,4]. ese key exchange protocols are fundamentally based on algebraic number theory [5]. In [21], the authors applied complex numbers to all internal components, instead of an integer system, to extend the original TPM model They showed that participants can exchange a pair of secret keys with a higher level of security. We propose a Vector-Valued Tree Parity Machine (VVTPM), which is an extended model of the basic TPM architecture, in which we apply a vectorvalued system to the internal components of the TPM. (1) A novel model of neural cryptography: we propose a novel model of neural cryptography, called VectorValued Tree Parity Machine, which can generate flexible lengths of secret keys by varying the number of vectors while increasing security and preserving the synchronization time.
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