An image encryption algorithm based on memristive chaotic neuron in medical internet of things environments.
Saved in:
| Title: | An image encryption algorithm based on memristive chaotic neuron in medical internet of things environments. |
|---|---|
| Authors: | Yu, Zhenhua1 (AUTHOR), Zhu, Kailong1 (AUTHOR), Song, Xinlin2 (AUTHOR), Yang, Feifei1 (AUTHOR) dlpuyff@sina.com |
| Source: | Engineering Applications of Artificial Intelligence. Jun2026, Vol. 173, pN.PAG-N.PAG. 1p. |
| Subjects: | Image encryption, Internet of medical things, Data protection, Cryptography |
| Abstract: | To address the severe security and privacy challenges faced by massive medical image data during transmission and storage in the medical Internet of Things environment, this paper proposes an image encryption algorithm based on memristive chaotic neuron. The encryption process adopts the classic architecture of scrambling-diffusion. During the scrambling stage, the image is adaptively divided into blocks. Within each sub-block, Zigzag scanning and rotation are randomly performed with equal probability to rearrange the pixel positions. Finally, the sub-blocks are globally randomly arranged to achieve pixel position scrambling in the deep space, break the spatial correlation between pixels. During the diffusion stage, based on the chaotic sequence, a dynamic Substitution box (S-box) is constructed to perform local XOR perturbation on the pixel values. Subsequently, the corresponding chaotic sequence is accumulated along the spiral path with the previous pixel value to achieve global gray-scale diffusion. The simulation experiments and security analysis show that this algorithm is extremely sensitive to the key, has a huge key space (2402), and can effectively resist various attack methods such as statistical analysis and brute-force attacks such as the ciphertext image has a correlation coefficient near zero and a uniform distribution, indicating successful encryption. The information entropy of the encrypted medical image is close to the theoretical maximum value, the correlation coefficient of adjacent pixels is close to zero, and the distribution is uniform. This algorithm ensures high security while also considering computational efficiency. It provides an effective solution for protecting patient privacy and the confidentiality of medical data. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
