Post-Quantum Cryptography Neural Network
This addresses the problem of securing communications against quantum attacks for applications like cellular networks, but it appears incremental as it builds on existing PQC methods by integrating them into a neural network framework.
This study tackled the threat of quantum computers to asymmetric cryptography by proposing a post-quantum cryptography (PQC)-based neural network that maps a code-based PQC method to a neural network structure, enhancing security with non-linear activation functions, random perturbation, and uniform distribution of ciphertexts, and demonstrated its feasibility in a cellular network signal case study for encryption and decryption.
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.