Blockchain-Enabled Privacy-Preserving Machine Learning Authentication With Immersive Devices for Urban Metaverse Cyberspaces

Abstract

Urban life has already embraced many urban metaverse use cases to increase the Quality of Life (QoL) by overcoming temporal and spatial restrictions, and the trend indicates that this would expedite exponentially in the years to come. Cybercommunities instilled with metaverse technologies should provide their residents with functional, safe, secure, and private worlds with high Quality of Experiences (QoE) to readily evolve and mitigate the problems of urbanisation. Cybersecurity and privacy protection are the two crucial challenges in making secure and reliable urban metaverse cyberspaces thrive, as cybercrime activities are expected to be rampant in this ecosystem with trillion dollars of economic value in the years to come. Ensuring seamless connectivity, data accuracy, and user privacy are critical aspects that need further attention for the efficacy of urban metaverse cyberspaces with Urban Twins (UTs), particularly, from technical, legislative, and ethical standpoints. A large number of transactions and immersive experiences shall be managed safely in an automated manner in urban metaverse cyberspaces. In this direction, this paper presents a blockchain-enabled method for immersive devicebased Decentralized Privacy-Preserving Machine Learning (BE-DPPML) authentication and verification. It can be effectively instrumented against identity theft and impersonation, as well as against the theft of credentials, identities, or avatars.