mlsanityData to 14 Oct 2026

Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey

arXiv 2310.10665 ↗First posted 19 Sep 2023cs.CR cs.AI cs.LGcited by 3

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Abstract (arXiv)

The metaverse is a nascent concept that envisions a virtual universe, a collaborative space where individuals can interact, create, and participate in a wide range of activities. Privacy in the metaverse is a critical concern as the concept evolves and immersive virtual experiences become more prevalent. The metaverse privacy problem refers to the challenges and concerns surrounding the privacy of personal information and data within Virtual Reality (VR) environments as the concept of a shared VR space becomes more accessible. Metaverse will harness advancements from various technologies such as Artificial Intelligence (AI), Extended Reality (XR), Mixed Reality (MR), and 5G/6G-based communication to provide personalized and immersive services to its users. Moreover, to enable more personalized experiences, the metaverse relies on the collection of fine-grained user data that leads to various privacy issues. Therefore, before the potential of the metaverse can be fully realized, privacy concerns related to personal information and data within VR environments must be addressed. This includes safeguarding users' control over their data, ensuring the security of their personal information, and protecting in-world actions and interactions from unauthorized sharing. In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions. Moreover, we thoroughly analyze technical solutions such as differential privacy, Homomorphic Encryption (HE), and Federated Learning (FL) and discuss related sociotechnical issues regarding privacy.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 66 of its 220 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

66 of 220 references.

References · 66

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38 more
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50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026PRISM-XR: Empowering Privacy-Aware XR Collaboration with Multimodal Large Language Modelscited by 1
  2. 2025A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Reviewcited by 0
  3. 2025Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networkscited by 0