PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem
Version 1
: Received: 5 February 2024 / Approved: 6 February 2024 / Online: 6 February 2024 (10:32:40 CET)
How to cite:
Kuru, K.; Kuru, K. Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem. Preprints2024, 2024020317. https://doi.org/10.20944/preprints202402.0317.v1
Kuru, K.; Kuru, K. Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem. Preprints 2024, 2024020317. https://doi.org/10.20944/preprints202402.0317.v1
Kuru, K.; Kuru, K. Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem. Preprints2024, 2024020317. https://doi.org/10.20944/preprints202402.0317.v1
APA Style
Kuru, K., & Kuru, K. (2024). Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem. Preprints. https://doi.org/10.20944/preprints202402.0317.v1
Chicago/Turabian Style
Kuru, K. and Kaan Kuru. 2024 "Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem" Preprints. https://doi.org/10.20944/preprints202402.0317.v1
Abstract
Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.
Keywords
Metaverse; Smart City (SC); Digital Twins (DTs); cybersecurity; Swarm Artificial Intelligence (SAI); Collaborative Deep Learning (CDL); Federated Learning (FL); Privacy-Preserving Machine Learning (PPML); blockchain; avatar
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
