An Effective Cloud Image Management Scheme Based on an n-Dimensional Hyperchaotic Map with Desired Lyapunov Exponents
Ziwen Zhu, Geng Zhao, Lingfeng Qu, Yingjie Ma, Yuan Yuan · International Journal of Bifurcation and Chaos · 2025
With the increasing popularity of cloud storage for its scalability and accessibility, data privacy has become a significant concern, as most mainstream cloud services store data in plaintext, exposing it to potential breaches. This paper introduces a novel image privacy protection scheme leveraging an innovative chaotic system, addressing the limitations of existing schemes. Chaotic systems are widely recognized for their utility in image encryption, particularly in scrambling and diffusion operations. Utilizing the Gershgorin circle theorem, we propose a method to generate n-dimensional nonlinear discrete maps with maximum n positive Lyapunov exponents, which are adjustable and user-specified. The generated chaotic sequences are employed for image scrambling and diffusion. The proposed scheme integrates Reversible Data Hiding (RDH) and Thumbnail-Preserving Encryption (TPE) to ensure that encrypted images retain visual features and enable the embedding of auxiliary data. This integration ensures that encrypted images maintain the same thumbnail and a high probability of the same sum of pixel values as the original images. Experimental results demonstrate that our scheme offers a robust solution for managing encrypted images in the cloud while ensuring data privacy and usability.