Artificial Immune Based Cryptography Optimization Algorithm

In the paper, an improved clone selection algorithm for cryptography optimization is proposed, the algorithm integrates genetic algorithm with immune computing and makes use of reproduction and mutation operator to maintain the diversity and optimization of candidate objects. As an experiment of the clone algorithm, a blind signcryption scheme with immune optimized parameter is proposed. In the signcryption scheme, parameters generated with clone selection have relatively higher level of fitness and thus avoids the arbitrary selection of essential parameters. Then we analyze the efficiency and feasibility of immune optimization algorithms with experiment data from the signcryption scheme. The reproduction operator in the algorithm can greatly improve the fitness level of candidate group, while the mutation operator effectively maintains the diversity of candidate individuals. In the experiment, the optimization coefficient (OC) reaches 0.9301 when the clone algorithm is executed just once. Lastly, we make detailed comparison between the optimized signcryption scheme and other typical schemes, including the blind signature of D.Chaum and the ECDSA signature. The data from the experiment and comparison show that the optimization algorithm can effectively improve the efficiency and accuracy of parameter optimization in cryptography systems.