Biblio

Big data of mobile network privacy is vulnerable to clear text attack in the process of storage and mixed network information sharing, which leads to information leakage. Through the mixed encryption of data of mobile network privacy big data to improve the confidentiality and security of mobile network privacy big data, a mobile network privacy big data hybrid encryption algorithm based on hyperchaos theory is proposed. The hybrid encryption key of mobile network privacy big data is constructed by using hyperchaotic nonlinear mapping hybrid encryption technology. Combined with the feature distribution of mobile network privacy big data, the mixed encrypted public key is designed by using Logistic hyperchaotic arrangement method, and a hyperchaotic analytic cipher and block cipher are constructed by using Rossle chaotic mapping. The random piecewise linear combination method is used to design the coding and key of mobile network privacy big data. According to the two-dimensional coding characteristics of mobile network privacy big data in the key authorization protocol, the hybrid encryption and decryption key of mobile network privacy big data is designed, and the mixed encryption and decryption key of mobile network privacy big data is constructed, Realize the privacy of mobile network big data mixed encryption output and key design. The simulation results show that this method has good confidentiality and strong steganography performance, which improves the anti-attack ability of big data, which is used to encrypt the privacy of mobile network.

Swarm intelligence is rather a simple implementation but has a good performance in function optimization. There are a variety of instances of swarm model and has its inherent dynamic property. In this study we consider a hybrid swarm model where agents complement each other using its native property. Employing popular swarm intelligence model Particle swarm and Firefly we consider hybridization methods in this study. This paper presents a hybridization that agents in Particle swarm selected by a simple rule or a random choice are changing its property to Firefly. Numerical studies are carried out by using complex function optimization benchmarks, the proposed method gives better performance compared with standard PSO.