Encryption Design and Analysis of 3-D Medical Models in Internet of Medical Things Using a Novel Memristive Hyperchaotic Map
Qiang Lai, Hanqiang Hua, Liang Yang
Abstract
In recent years, network attacks on medical information have posed a significant threat to the development of the Internet of Medical Things (IoMT). To address these threats, this paper proposes a novel chaos-based encryption scheme for 3D medical models. First, a new memristive hyperchaotic map (LC-CMHM) is designed. Simulation results demonstrate that, compared to classical chaotic maps, LC-CMHM exhibits stronger ergodicity and unpredictability, ensuring security for the encryption algorithm. Additionally, hardware implementation verifies its feasibility. Utilizing pseudo-random numbers generated by LC-CMHM, a tailored encryption algorithm is developed specifically for the structural features of 3D medical models. This algorithm encrypts both the faces and vertices of the 3D model, disrupting inherent correlations to enhance encryption effectiveness. Performance analysis confirms that the proposed scheme effectively transforms meaningful 3D medical data into a chaotic, unrecognizable sequence, providing strong resistance against differential attacks, chosen-plaintext attacks, and statistical attacks.