Memristive Neural Network With Controllable Extreme Multistability and Its Application in Multi-Type Medical Multimedia Data Encryption

Medical institutions store a vast amount of patient information, and various types of medical data face severe security challenges in cloud storage environments. This paper proposes an efficient multi-type medical multimedia data encryption scheme based on the memristive Hopfield neural network (MHNN). First, a class of MHNNs is constructed, which exhibits isomorphic extreme multistability under different initial conditions. The systems are capable of generating large-scale coexisting chaotic attractors, whose spatial positions, vortex numbers, and amplitudes can be independently regulated, thereby significantly expanding the generation capacity and diversity of chaotic sequences. Based on this, a multi-type data encryption algorithm is designed, which uniformly encodes and integrates different types of medical data into a secure transmission structure. Furthermore, by exploiting multistable characteristics of the MHNN, a two-layer key system composed of a master key and a selection key is established, enabling a secure medical data scheme that supports multi-party collaborative annotation. Experimental results demonstrate that the proposed encryption scheme achieves excellent performance in both security and efficiency, providing a novel and effective solution for secure storage and collaborative processing of multi-type medical multimedia data.