SoK：扩展现实中的运动数据隐私

中文摘要 AI 辅助

扩展现实（Extended Reality，XR）提供沉浸式、交互式的3D体验，为实现这些体验，设备必须追踪用户运动，以便系统响应抓取、查看或移动物体等动作。然而，运动追踪引发了隐私担忧，因为它会记录人的运动模式，这些模式已在步态识别与画像等多个领域被广泛研究，且被证实能揭示敏感信息。随着XR的普及，这些模式比以往任何时候都更容易被记录和获取，由此产生了根本性的隐私矛盾：运动追踪是XR核心功能的基础，却要求用户牺牲隐私。此前关于XR隐私的系统化知识（systematization-of-knowledge，SoK）研究已广泛考察该领域，与运动相关的研究分布在多个隐私领域，未被视为独立研究方向。但自上次SoK以来，XR运动隐私研究发展迅速，相关文献规模几乎增至原来的四倍，因此有必要对该主题进行专门的系统化整理。本SoK考察了134篇与XR头显记录的运动模式隐私担忧相关的论文，包括攻击者如何获取用户运动模式、能从中推断出哪些信息，以及保护用户的方法。基于该综述，我们综合了运动模态、表示方式和推断风险的分类体系，构建了XR运动威胁模型，系统化了XR运动文献中的攻击与防御方法，指出了现有研究的不足，并为未来评估运动隐私机制的研究提供了指导。总体而言，本SoK阐明了XR运动隐私的现状，并为未来的评估工作提供了建议。

英文摘要

Extended Reality (XR) provides immersive, interactive 3D experiences. To enable these experiences, the devices must track user motion so the system can respond to actions such as grabbing, looking at, or moving an object. However, motion tracking has raised privacy concerns since it records a person's motion patterns. These motion patterns have been studied extensively across various fields (i.e., gait identification and profiling) and have been shown to reveal sensitive information. With the adoption of XR, these patterns became easier to record and obtain than ever. This creates a fundamental privacy tension: motion tracking enables core XR functionality yet requires users to compromise their privacy. Prior systematization-of-knowledge (SoK) studies on XR privacy have examined the field broadly, with motion-related research distributed across several privacy domains rather than treated as a distinct area of study. However, XR motion privacy has gained significant momentum since the prior SoK, with the literature nearly quadrupling in size and thereby warranting a dedicated systematization of this topic. This SoK examines 134 relevant papers on privacy concerns in motion patterns recorded by XR headsets, including how adversaries can obtain users' motion patterns, the inferences they can draw from them, and methods for protecting users. Based on this review, we synthesize a taxonomy of motion modalities, representations, and inference risks; develop an XR motion threat model; systematize the attack and defense approaches in the XR motion literature; identify gaps in the literature; and provide guidelines for future studies evaluating motion privacy mechanisms. Together, our SoK clarifies the state of XR motion privacy and provides recommendations for future evaluations.

发表机构

• University of Florida（佛罗里达大学）

机构由 AI 辅助整理，请以论文原文为准。