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.

With the growing demand for cloud services, traditional image privacy encryption methods applied in cloud scenarios reveal two major issues. First, security is often achieved at the expense of visibility, which is incompatible with cloud service scenarios such as information preview and search. Second, there is a lack of design for hierarchical visual privacy for users with different security levels. For the above problems, a multi-level key mechanism is designed and integrated with the YoloV5 network, providing not only multi-level privacy protection for sensitive regions but also achieving a balance between visibility and security in these regions. Simulation results demonstrate that the proposed framework can decrypt images with multi-level visual effects. Performance analysis shows that the framework achieves an adjustable balance between visibility and security, which users can modify by adjusting parameters. Compared to other visibility-security trade-off schemes, this approach offers advantages including strong reversibility, high image size compatibility, adjustable visual effects, and computational efficiency.

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%.

With the popularity of surveillance devices, the security of surveillance video has attracted much attention, and three key issues need to be solved. The videos cannot be synchronized with their encryption effects, while full encryption does not meet the current trend of lightweight algorithms, and customized encryption for multiple specific targets is rarely seen. Inspired by this, a lightweight batch video frame awareness multitarget encryption scheme based on a novel discrete Tabu learning neuron (DTLN) and YoloV5 is designed in this article, the DTLN is in hyperchaotic state within a great range of parameters, which ensures the diversity of key selection and security. At the same time, the coexistence of homogeneous attractors is found, and such attractors are difficult to be successfully recognized by the parameter recognition algorithm, which increases the difficulty for the attacker to obtain the key. The mosaic tracking scheme designed by YoloV5 network can lightweightly encrypt batch frames of multiple types and targets, and users can also customize the encryption targets according to their needs. The simulation results show that the encryption scheme can realize lightweight encryption of multitype and multitarget, and performs well in all the security performance indexes, and has certain advantages compared with other video encryption schemes in terms of performance and functionality.

Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency

In the previous encryption schemes for sensor information security in WBANs, there are usually the disadvantages of high cost and low time efficiency. At the same time, a large amount of information flooding into the channel also tends to bring huge transmission pressure to the channel. Motivated by the above viewpoints, a scheme that combines semi-tensor product compressed sensing (STP-CS) and session keys (SesKs) for low-cost batch information encryption in WBANs is proposed using image information as an example. The STP-CS can not only compress the information under the guarantee of high reconstruction quality, which reduces the pressure on the channel to transmit the information but also solves the problem of weak compatibility of traditional compressed sensing (CS) on the image size. The SesKs obtained by the communicating parties through the negotiation of the secret keys (SKs) cannot be returned to the SKs for a limited period of time. The cost of achieving one-time pad encryption is also lower compared to the traditional stream cipher encryption, which improves the robustness of wireless broadband network transmission. In terms of the encryption algorithms, the batch image encryption algorithms ensure the timeliness of information transmission security. The results show that the key space of the scheme is as high as 2869, the reconstructed image of$256\times 256$under compression ratio of 0.75 can still obtain PSNR as high as 45.94 dB, and the information entropy of the encrypted image is as high as 7.9978. Compared with the other encryption schemes, the scheme is more suitable to be applied to WBANs because it ensures sufficient security while having the features of low cost, high compatibility, and high efficiency.