Aiming at the problems of sensitive data leakage and unauthorised direct access faced during the storage and transmission of shared medical data, this paper proposes a new medical data security sharing scheme based on blockchain. Firstly, to ensure the security and randomness of the keys in the scheme, a new 4D-YG hyperchaotic key generator is designed. Secondly, self-expanding fractal Sierpinski triangle permutating operation is performed on medical images to achieve image decentring effect. Utilize a custom Order-Preserving Encryption (OPE) algorithm to diffuse it, ensuring consistency in pixel order before and after encryption. Subsequently, mix-bit triple normalized diffusion is performed on the images that have completed preliminary diffusion to enhance the resistance of privacy data within the image to attacks. Finally, the decryption permission for encrypted shared medical data is restricted through smart contracts in the blockchain. While achieving secure transmission of medical data, it enables hospitals to perform medical image segmentation and organ recognition diagnostics on shared data in an encrypted state, which is more in line with practical needs. According to performance test data, the proposed encryption algorithm can effectively resist differential attacks and frequency attacks, and the Number of Pixels Change Rate (NPCR) test results can reach the ideal value of 99.6094%.

To address the security risks in sharing medical imaging data and the challenges of insufficient data interoperability between heterogeneous systems, and to achieve secure sharing of medical information across institutions, this paper proposes a secure encryption scheme based on the characteristics of medical images, and uses cloud storage and smart contracts to build a secure framework for cross-institutional information sharing. First, in response to the regional characteristics and uneven information distribution of medical images, an innovative two-stage encryption scheme of “partitioning, diffusion, and coupled scrambling” is proposed. The first stage applies information entropy equalisation processing to non-ROI regions, enhancing their information effectiveness and generating dynamic parameters. The second stage utilises the cross-region coupling dynamic parameters generated in the first stage to optimise the encryption results within ROI regions. Finally, a key driven scrambling factor is employed to complete the global pixel position reconstruction, achieving high-security encryption. Each medical institution encrypts medical images and uploads them to unified cloud storage, along with binary thumbnails generated from the corresponding plaintext images. Simultaneously, essential metadata—including image hash values, ownership details, and access permission policies—is stored on the blockchain. After an authorized user selects the desired data by browsing cloud thumbnails, a smart contract automatically executes the transaction, completing on-chain permission verification, data traceability, and access authentication, thereby coordinating the user's secure access to the corresponding encrypted image data off-chain. Eight experiments have demonstrated the security of the encryption scheme and the proposed sharing framework.

As driverless technology advances, an immense amount of data will be generated, shared, and used between vehicles, users, and the cloud. This includes road conditions, GPS data, biometric data, which raises significant privacy concerns. This paper presents a secure authentication protocol and privacy protection framework based on chaotic biological cryptography for data sharing and storage in the Internet of Autonomous Vehicles(IoAV) framework. A biometric key generator is designed using single-modal multi-fingerprint feature-level reconstruction, allowing users to generate personalized private key pools. This protects biometric data in the cloud while ensuring revocability. By incorporating a nonlinear control function into the traditional three-dimensional Rucklidge chaotic system, a 4D-Yoz hyperchaotic system is created, ensuring secure key distribution and adherence to the “one time pad” principle. To verify the effectiveness of the chaos creature password and ensure the security of cloud-stored images, proposes an image encryption-storage algorithm based on the “$L_{\infty } $” metric and the semi-tensor product of the matrix, introducing the chaotic biological key. Experimental simulations and performance analysis show that this algorithm effectively secures image data in the Internet of Vehicles.

The rapid expansion of smart devices and applications within the Internet of Things (IoT) has resulted in an unprecedented surge of data, particularly image data, generated at the network edge. This imposes significant pressure on traditional centralized cloud computing paradigms and introduces severe challenges in terms of transmission efficiency and data security. To address these issues, edge computing-assisted IoT (EC-IoT) has emerged as a promising paradigm; however, the decentralized collection, transmission, and application of image data also exacerbate privacy risks. In this work, this article proposes a secure and lightweight EC-IoT architecture specifically tailored for efficient and resilient image transmission. Node registration and tagging mechanisms are introduced to enable identity verification without additional computational overhead, while Z-order encoding, strict hierarchical encryption, and node verification collectively ensure robust protection at minimal cost. Furthermore, we design an image encryption framework that integrates a novel 4-D cross-coupled chaotic map (4DCCM) with lattice-based cryptography, achieving strong encryption performance and seamless post-quantum security compatibility. Extensive experimental evaluations conducted on representative IoT application datasets—including Internet of Medical Things (IoMT), Internet of Vehicles (IoV), and Industrial IoT (IIoT) scenarios—demonstrate the effectiveness of the proposed scheme, achieving an average information entropy of 7.9993 while maintaining low computational overhead. This work contributes a secure, efficient, and post-quantum-resilient encryption framework, particularly suited for large-scale real-time transmission of visual information within next-generation IoT environments.

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 A bit‐level image encryption algorithm based on Fully‐Connected‐Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security.

Abstract To mitigate the shortcomings of existing medical image encryption algorithms, including a lack of anti‐tampering methods and security, this report presents an anti‐tampering encryption algorithm for medical images that is based on a self‐verification matrix. First, chaotic coordinates generated by chaos are used to traverse all pixels in a plain image to generate a two‐dimensional matrix (a self‐verification matrix) with positioning information. The accurate location of illegally altered image pixels can be detected using the self‐verification matrix. To improve the security of the self‐authentication matrix, DNA coding is also applied to the self‐authentication matrix, and the plain image is also diffused statically to destroy the pixel distribution. Next, the scrambled image and self‐verification matrix are mixed and cross‐scrambled. Finally, the fused image is diffused dynamically to improve the security of the encrypted image. Experimental simulation and performance analysis show that the algorithm achieves good encryption effectiveness, provides strong anti‐tampering capabilities, and can accurately locate at least 4 pixels.

Abstract In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.