UMetaBE-DPPML: Urban metaverse & blockchain-enabled decentralised privacy-preserving machine learning verification and authentication with metaverse immersive devices

Abstract

It is anticipated that cybercrime activities will be widespread in the urban metaverse ecosystem due to its high economic value with new types of assets and its immersive nature with a variety of experiences. Ensuring reliable urban metaverse cyberspaces requires addressing two critical challenges, namely, cybersecurity and privacy protection. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification methodology, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars. Blockchain technology and Federated Learning (FL) are merged in the developed DPPML approach not only to eliminate the requirement of a trusted third party for the verification of the authenticity of transactions and immersive actions, but also, to avoid Single Point of Failure (SPoF) and Generative Adversarial Networks (GAN) attacks by detecting malicious nodes. The developed methodology has been tested using Motion Capture Suits (MoCaps) in a co-simulation environment with the Proof-of-Work (PoW) consensus mechanism. The preliminary results suggest that the built techniques in the DPPML approach can prevent unreal transactions, impersonation, identity theft, and theft of credentials or avatars promptly before any transactions have been executed or immersive experiences have been shared with others. The proposed system will be tested with a larger number of nodes involving the Proof-of-Stake (PoS) consensus mechanism using several other metaverse immersive devices as a future job.