Securing Privacy in the Metaverse

Abstract

This research explores the integration of privacy-preserving federated learning techniques in the context of biomedical image analysis within the metaverse. The metaverse, a virtual shared space, has witnessed remarkable advancements in various fields, including healthcare. However, ensuring the confidentiality of sensitive medical data poses a significant challenge. This study proposes a novel approach to address this concern by employing federated learning, a collaborative machine learning paradigm that enables model training across decentralized devices without compromising individual data privacy. The investigation focuses on the application of these techniques to enhance biomedical image analysis within the metaverse, aiming to facilitate medical research and diagnosis. Through the implementation of secure and privacy-preserving federated learning, the authors aim to strike a balance between technological innovation and safeguarding sensitive health information in the evolving landscape of the metaverse.

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