In recent years, quantum computers and Shor quantum algorithm have posed a threat to current mainstream asymmetric cryptography methods (e.g. RSA and Elliptic Curve Cryptography (ECC)). Therefore, it is necessary to construct a Post-Quantum Cryptography (PQC) method to resist quantum computing attacks. Therefore, this study proposes a PQC-based neural network that maps a code-based PQC method to a neural network structure and enhances the security of ciphertexts with non-linear activation functions, random perturbation of ciphertexts, and uniform distribution of ciphertexts. In practical experiments, this study uses cellular network signals as a case study to demonstrate that encryption and decryption can be performed by the proposed PQC-based neural network with the uniform distribution of ciphertexts. In the future, the proposed PQC-based neural network could be applied to various applications.

翻译：近年来，量子计算机和Shor量子算法对当前主流非对称密码方法（如RSA和椭圆曲线密码学（ECC））构成了威胁。因此，有必要构建一种后量子密码（PQC）方法来抵御量子计算攻击。为此，本研究提出了一种基于PQC的神经网络，将基于编码的PQC方法映射到神经网络结构，并通过非线性激活函数、密文随机扰动以及密文均匀分布来增强密文安全性。在实际实验中，本研究以蜂窝网络信号为例，证明所提出的基于PQC的神经网络能够在密文均匀分布条件下实现加密与解密。未来，该基于PQC的神经网络可应用于多种场景。