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As the metaverse grows with the advances of new technologies, a number of researchers have raised the concern on the privacy of motion data in virtual reality (VR). It is becoming clear that motion data can reveal essential information of people, such as user identification. However, the fundamental problems about what types of motion data, how to process, and on what ranges of VR applications are still underexplored. This work summarizes the work of motion data privacy on these aspects from both the fields of VR and data privacy. Our results demonstrate that researchers from both fields have recognized the importance of the problem, while there are differences due to the focused problems. A variety of VR studies have been used for user identification, and the results are affected by the application types and ranges of involved actions. We also review the biometrics work from related fields including the behaviors of keystrokes and waist as well as data of skeleton, face and fingerprint. At the end, we discuss our findings and suggest future work to protect the privacy of motion data.
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Linkage Attack on Skeleton-based Motion Visualization
Skeleton-based motion capture and visualization is an important computer vision task, especially in the virtual reality (VR) envi- ronment. It has grown increasingly popular due to the ease of gathering skeleton data and the high demand of virtual socializa- tion. The captured skeleton data seems anonymous but can still be used to extract personal identifiable information (PII). This can lead to an unintended privacy leakage inside a VR meta-verse. We propose a novel linkage attack on skeleton-based motion visual- ization. It detects if a target and a reference skeleton are the same individual. The proposed model, called Linkage Attack Neural Net- work (LAN), is based on the principles of a Siamese Network. It incorporates deep neural networks to embed the relevant PII then uses a classifier to match the reference and target skeletons. We also employ classical and deep motion retargeting (MR) to cast the target skeleton onto a dummy skeleton such that the motion sequence is anonymized for privacy protection. Our evaluation shows that the effectiveness of LAN in the linkage attack and the effectiveness of MR in anonymization. The source code is available at https://github.com/Thomasc33/Linkage-Attack
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- Award ID(s):
- 1840080
- PAR ID:
- 10464820
- Date Published:
- Journal Name:
- ACM International Conference on Information and Knowledge Management
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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