S, Vijitha and R, Anandan (2025) Blockchain-based decentralized identifier in metaverse environment for secure and privacy-preserving authentication with improved key management and cryptosystem. Peer-to-Peer Networking and Applications, 18 (4). ISSN 1936-6442
Full text not available from this repository. (Request a copy)Abstract
The metaverse is gaining popularity because it offers a virtual environment with social interactions that are similar to those in the real world. Because of this heightened attention, protecting privacy and security is becoming more and more crucial. Users can create a variety of avatars in the metaverse, which presents internal security issues if misused and leads to deceptive or dangerous behavior. Metaverse in e-learning platforms becomes an interesting aspect in the current scenario. However, users trying access to the metaverse are exposed to a number of external security threats because they interact with service providers through open channels. To overcome this issue, a novel privacy-preserving and secure authentication protocol using Improved Light Weight key management-based CryptoSystem (ILWKM-CS) is proposed. The proposed ILWKM-CS scheme for privacy-preserving and secure authentication comprises four distinct phases: User setup, User Registration, Login, and Avatar authentication. In the User setup phase, individuals establish their Decentralized Identifiers (DID), and a verifiable credential is issued by the central authority as evidence of the personal data of the user. The next step is user registration, where users create an avatar in the virtual space and register using their DID. Using the ILWKM system, the user and the service provider authenticate each other during the login phase. Lastly, avatars interact with other avatars within the virtual environment and authentication is ensured between them using the MECC technique in the Avatar authentication phase. Moreover, the optimal key is generated to encrypt the message via the proposed hybrid optimization TSAOO algorithm.
Item Type: | Article |
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Subjects: | Computer Science Engineering > Automated Machine Learning |
Domains: | Computer Science Engineering |
Depositing User: | Mr IR Admin |
Date Deposited: | 08 Aug 2025 09:24 |
Last Modified: | 08 Aug 2025 09:24 |
URI: | https://ir.vistas.ac.in/id/eprint/9891 |