title: Multivariate Cryptography Based on Clipped Hopfield Neural Network.
abstract: Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.
authors.forename: Jia
authors.initials: J
authors.lastname: Wang
authors.forename: Lee-Ming
authors.initials: LM
authors.lastname: Cheng
authors.forename: Tong
authors.initials: T
authors.lastname: Su