Privacy bubbles for digital twin interaction in the metaverse

Abstract

Digital Twins (DTs) are increasingly integral to the metaverse, enabling real-time simulation, monitoring, and optimization of physical assets and processes in a fully digital environment. However, as DTs become richer in functionality and data, ensuring that users can easily and intuitively control what information is shared becomes critical. This paper introduces a novel user interface concept—the Privacy Bubble—that provides dynamic, visually intuitive privacy management for DT interactions. By allowing users to seamlessly adjust the visibility and exposure of their DT’s sensitive attributes, the Privacy Bubble enhances trust, security, and user confidence in the metaverse. Our initial evaluations indicate that this new interface paradigm improves both usability and user satisfaction, laying the foundation for more secure and humancentric DT ecosystems.

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