Mu Nankun, Liao Xiaofeng, Huang Tingwen
College of Computer Science, Chongqing University, Chongqing, 400044, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jun;87(6):062804. doi: 10.1103/PhysRevE.87.062804. Epub 2013 Jun 12.
Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.
神经密码学是一种公钥交换协议，被广泛认为是在公共信道上两个神经网络之间共享通用密钥的有效方法。如何设计神经密码学仍然是一个巨大的挑战。在本文中，为了提供一种解决这一挑战的方法，设计了一种广义网络架构和一条重要的启发式规则。所提出的通用框架被命名为树状状态分类机（TSCM），它扩展并统一了现有结构，即树奇偶机（TPM）和树委员会机（TCM）。此外，我们仔细研究并发现，该启发式规则可以提高基于TSCM的神经密码学的安全性。因此，TSCM和启发式规则可以指导我们设计大量有效的神经密码学候选方案，其中有可能实现更安全的实例。值得注意的是，根据TSCM和启发式规则，我们进一步阐述了我们设计的神经密码学在安全性方面优于TPM（目前最安全的模型）。最后，提供了一系列数值模拟实验来验证我们结果的有效性和适用性。
