Privacy Preservation of Large Language Models in the Metaverse Era:
Research Frontiers, Categorical Comparisons, and Future Directions
Abstract
Large language models (LLMs), with their billions to trillions
of parameters, excel in natural language processing, machine
translation, dialogue systems, and text summarization. These
capabilities are increasingly pivotal in the Metaverse, where they can
enhance virtual interactions and environments. However, their extensive
use, particularly in the Metaverse’s immersive platforms, raises
significant privacy concerns. This paper analyzes existing privacy
issues in LLMs, vital for both traditional and Metaverse applications,
and examines protection techniques across the entire life cycle of these
models, from training to user deployment. We delve into cryptography,
embedding layer encoding, differential privacy and its variants, and
adversarial networks, highlighting their relevance in the Metaverse
context. Specifically, we explore technologies like homomorphic
encryption and secure multi-party computation, which are essential for
Metaverse security. Our discussion on Gaussian differential privacy,
Renyi differential privacy, Edgeworth accounting, and the generation of
adversarial samples and loss functions, emphasizes their importance in
the Metaverse’s dynamic and interactive environments. Lastly, the paper
discusses the current research status and future challenges in the
security of LLMs within and beyond the Metaverse, emphasizing urgent
problems and potential areas for exploration.
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