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A Framework for Quantifying Privacy Risks in the 6G Metaverse: A Hybrid AI Attack
Abstract
The metaverse is being heralded as the next stage in the evolution of the internet and promises to provide every individual on Earth with a fully immersive digital reality. The emerging ecosystem, based on the advent of 6G networks and a host of enablers, will challenge existing trends in socialization and commerce, along with industry. However, with this ambition comes new and perhaps the most serious challenges for privacy in creating a universe that is persistent and rich in data. In this chapter, we propose an additional novel kind of hybrid AI-attack vector that synergizes with membership inference and reconstruction attacks, posing threats to user anonymity. The chapter will also look for counteractive measures with the feasibility of advanced cryptographic protocols, differential privacy, and federated learning architectures as the bedrock toward a trustworthy and secure metaverse. These findings express the urgent need for privacy-by-design to be the guiding principle of creating these new virtual worlds, rather than an afterthought.
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