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<p>As the Metaverse advances, securing three-dimensional (3D) digital assets, particularly in the GL Transmission Format Binary (GLB) file format, is crucial due to their lightweight, self-contained nature and efficient binary encoding, which contributes to their compact size and fast load times essential for real-time rendering in gaming and interactive applications. However, the binary encoding and complex structure of GLB files present significant encryption challenges. This research presents a novel extraction framework for the vertex data of 3D GLB objects to facilitate their encryption, ensuring the secure management of Metaverse assets. It should be noted that the proposed system is not itself a new encryption algorithm, but rather a pre-encryption preparation stage that outputs vertex and texture data in structured arrays, which can then be serialized for use with any standard or advanced encryption scheme. The motivation for this framework lies in safeguarding high-value digital assets in the Metaverse, such as rare jewelry designs, unreleased fashion pieces, concept cars, or game assets that must remain undisclosed until release, where confidentiality, intellectual property rights, and security outweigh real-time speed considerations. The proposed system focuses on extracting the x, y, and z vertex coordinates of the GLB, making them more suitable for encryption, especially using the Improved Flexible Cryptosystem based on Cellular Automata. By leveraging the binary structure and multi-layered nature of GLB files, the proposed system ensures both efficiency and adaptability in encryption and decryption processes. Furthermore, the evaluation of implementation results demonstrated the system’s usability, performance, and security in Metaverse applications.</p>

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3D GLB object extraction framework for encrypting metaverse assets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

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  • Manar A. Mizher,
  • Manal A. Mizher,
  • Ahmad A. Mazhar,
  • Ayman M. Abdallah,
  • Aman S. Altamimi

摘要

As the Metaverse advances, securing three-dimensional (3D) digital assets, particularly in the GL Transmission Format Binary (GLB) file format, is crucial due to their lightweight, self-contained nature and efficient binary encoding, which contributes to their compact size and fast load times essential for real-time rendering in gaming and interactive applications. However, the binary encoding and complex structure of GLB files present significant encryption challenges. This research presents a novel extraction framework for the vertex data of 3D GLB objects to facilitate their encryption, ensuring the secure management of Metaverse assets. It should be noted that the proposed system is not itself a new encryption algorithm, but rather a pre-encryption preparation stage that outputs vertex and texture data in structured arrays, which can then be serialized for use with any standard or advanced encryption scheme. The motivation for this framework lies in safeguarding high-value digital assets in the Metaverse, such as rare jewelry designs, unreleased fashion pieces, concept cars, or game assets that must remain undisclosed until release, where confidentiality, intellectual property rights, and security outweigh real-time speed considerations. The proposed system focuses on extracting the x, y, and z vertex coordinates of the GLB, making them more suitable for encryption, especially using the Improved Flexible Cryptosystem based on Cellular Automata. By leveraging the binary structure and multi-layered nature of GLB files, the proposed system ensures both efficiency and adaptability in encryption and decryption processes. Furthermore, the evaluation of implementation results demonstrated the system’s usability, performance, and security in Metaverse applications.