As industrial systems increasingly rely on 3D mesh models to bridge the physical and digital domains, ensuring their secure and efficient management has become critical. While thumbnail-preserving encryption (TPE) has successfully balanced security and usability for 2D images, extending this concept to 3D models remains largely unexplored. In this work, a novel distributed trust and visually meaningful secret sharing scheme for 3D mesh models (3D-VMSS) is proposed. The distributed trust mechanism splits the model data among multiple participants, ensuring that no single party possesses sufficient information for reconstruction. The original model can only be reconstructed through collaboration among a predefined threshold of authenticated participants. This approach fundamentally differs from traditional single-key encryption by eliminating single points of failure and enabling flexible access control. The scheme segments vertex coordinates into hierarchical components and applies polynomial secret sharing to ensure confidentiality, generating visually meaningful shares that preserve recognizable geometric features while concealing sensitive details. To ensure integrity and resist collusion attacks, dual authentication mechanisms are incorporated. Furthermore, progressive reconstruction enables different quality levels based on participant collaboration. Experimental results demonstrate the scheme's effectiveness in balancing security and practical usability for distributed 3D model management.

Reversible data hiding (RDH) provides a practical solution for the secure storage and transmission of sensitive data in cloud environments. With the increasing adoption of three-dimensional (3D) mesh models in fields that require high privacy and intellectual property protection, RDH techniques specifically designed for these models have gained considerable attention. However, existing RDH schemes for 3D mesh models often encounter limitations such as a low embedding capacity, low runtime efficiency, or insufficient security. To address these challenges, this paper proposes a novel privacy preservation scheme that integrates the geometric projection strategy with Elliptic Curve Integrated Encryption (ECIE). The geometric projection strategy effectively exploits local geometric regularities within mesh models, thereby enhancing the vertex prediction accuracy. The integration of ECIE into the RDH framework further strengthens security by mitigating the risks associated with symmetric key transmission, providing enhanced protection tailored to customized data. Experimental results demonstrate that compared to state-of-the-art methods, the proposed scheme achieves superior embedding capacity and vertex utilization rate while maintaining perfect reversibility, separable data extraction, and high runtime efficiency.

Reversible data hiding in encrypted 3D models (RDHEM) is an emerging steganography technique, capable of both encrypting the cover model to ensure confidentiality and embedding additional messages for covert communication. However, the embedding capacity provided by recent RDHEM methods is still at a low level. In this paper, an adaptive vertex grouping strategy is proposed, which can divide the vertices in the cover 3D model into groups. Then, the multi-MSB prediction and Huffman coding are exploited to compress the data volume of vertices. Through proper vertex grouping and efficient data compression of the model vertices, the embedding capacity of the RDHEM can be effectively improved. Additionally, two schemes for 3D model encryption are provided. One is based on a secret sharing method over the Galois field and the other leverages the stream cipher technique. Experimental results show that the embedding capacity of the two proposed schemes significantly outperforms state-of-the-art schemes.