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Neural Cryptography Based on Generalized Tree Parity Machine for Real-Life Systems
Security and Communication Networks ( IF 1.968 ) Pub Date : 2021-02-04 , DOI: 10.1155/2021/6680782
Sooyong Jeong 1 , Cheolhee Park 2 , Dowon Hong 2 , Changho Seo 1 , Namsu Jho 3
Affiliation  

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.

中文翻译:

基于广义树奇偶机的神经密码学在现实生活中的应用

传统的公钥交换协议基于代数数论。从另一个角度来看,基于神经网络的神经密码术已经出现。据报道,两方可以用神经网络中的同步现象交换秘密密钥对。尽管存在称为树奇偶校验机(TPM)的各种神经密码学模型,但考虑到效率和安全性,许多模型都不适合实际使用。在本文中,我们提出了一种矢量值树奇偶校验机(VVTPM),它是TPM模型的通用体系结构,对于现实生活中的系统可以更加有效和安全。在效率和安全性方面,我们表明VVTPM的同步时间与基本TPM模型的顺序相同,
更新日期:2021-02-04
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