Encryption Design and Analysis of 3-D Medical Models in Internet of Medical Things Using a Novel Memristive Hyperchaotic Map

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 article proposes a novel chaos-based encryption scheme for 3-D medical models. First, a new memristive hyperchaotic map [logistic and cosine-combinatorial 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 3-D medical models. This algorithm encrypts both the faces and vertices of the 3-D model, disrupting inherent correlations to enhance encryption effectiveness. Performance analysis confirms that the proposed scheme effectively transforms meaningful 3-D medical data into a chaotic, unrecognizable sequence, providing strong resistance against differential attacks, chosen-plaintext attacks, and statistical attacks.