Multi-Objective Evolution of Strong S-Boxes Using Non-Dominated Sorting Genetic Algorithm-II and Chaos for Secure Telemedicine

Abstract

There exist several performance criteria for cryptographically strong substitution boxes which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box designing algorithms are used to optimize only a single performance criterion, mainly the nonlinearity, which usually resulted in weak scores of other equally significant criteria. To overcome this problem, a multi-objective optimization based method is presented in this paper which constructs 8×8 S-boxes satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. The fulfillment of multiple objectives is done by applying the chaos-assisted non-dominated sorting genetic algorithm-II to evolve S-boxes. The performance assessment of a proposed method and comparative analysis with available optimization based and other state-of-the art algorithms demonstrate its proficiency to generate significantly better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum NL of 110, DU as low as 8, and ACF as low as 80, are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm makes use of S-box for performing the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.