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Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

Recently, it has been found that cloud storage still has security risks, and research on the security and privacy of user data and information is still in the early stage. This paper studies the security risks of cloud data, and designs an image encryption scheme based on neural networks. First, the existing neural network model is improved to obtain a new bidirectional activation (BA) neural network, to establish a many-to-one mapping relationship between the key and the chaotic initial value, to hide the original key of the cloud encryption system, and to improve the security and randomness of the key system. Then, a medical image encryption scheme based on dynamic index scrambling and the M-semitensor product diffusion is proposed. Dynamic index scrambling is more flexible than the traditional approach, and its security and efficiency are improved. The diffusion algorithm adopts the semi tensor product operation, and one of the product matrices is composed of a unitary matrix after Schur decomposition of a plaintext image to effectively resist a selective plaintext attack. Performance analysis shows that the encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

Biometric technology is increasingly being used in various areas, including government law enforcement, exit and entry management, visitor access control, user identification, and daily attendance management for companies. This technology simplifies the procedure of user identity verification and reduces the time required for verification. However, the security of biological data stored in the system can be overlooked. To address this, we propose a secure biometric feature matching framework that encrypts all information stored in the biometric system. The system decrypts and extracts feature values only when recognition and matching are required, enabling secure matching judgment. This framework protects private data in the biometric system from illegal theft, tampering, and destruction, and promotes research and practical application of biometric security technology. To fully ensure system data security, we propose an adaptive rotation diffusion image encryption algorithm based on double chaos. The algorithm uses double chaos to control image scrambling and leverages the random characteristics of chaos. The algorithm adopts six different rotation diffusion methods, and different plaintext images lead to completely different diffusion methods, making the algorithm adaptive and effective against selected plaintext attacks. Experimental simulations and performance analysis confirm the feasibility and superiority of the proposed algorithm. [Display omitted] • Build a secure biometric data protection framework. • Establish matching relationship between plaintext and ciphertext for biometric data. • Propose a plaintext related adaptive rotation diffusion encryption method. [ABSTRACT FROM AUTHOR]

To realize the secure transmission of double images, this paper proposes a double image encryption algorithm based on convolutional neural network (CNN) and dynamic adaptive diffusion. This scheme is different from the existing double image encryption technology. According to the characteristics of digital image, we design a dual-channel (digital channel / optical channel) encryption method, which not only ensures the security of double image, but also improves the encryption efficiency and reduces the possibility of being attacked. First, a chaotic map is used to control the initial values of the 5D conservative chaotic system to enhance the security of the key. Secondary, in order to effectively resist known-plaintext attack and chosen-plaintext attack, we employ a chaotic sequence as convolution kernel of convolution neural network to generate plaintext related chaotic pointer to control the scrambling operation of two images. On this basis, a novel image fusion method is designed, which splits and fuses two images into two different parts according to the amount of information contained. In addition, a dual-channel image encryption scheme, optical encryption channel and digital encryption channel, is designed for the two parts after fusion. The former has better parallelism and higher encryption efficiency, while the latter has higher computational complexity and better encryption reliability. Especially in the digital encryption channel, a new dynamic adaptive diffusion method is designed, which is more flexible and secure than the existing encryption algorithm. Finally, numerical simulation and experimental analysis verify the feasibility and effectiveness of the scheme. [ABSTRACT FROM AUTHOR]