Effect of Duration and Delay on the Identifiability of VR Motion
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909296754688000 |
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| author | Miller, Mark Roman Nair, Vivek Han, Eugy DeVeaux, Cyan Rack, Christian Wang, Rui Huang, Brandon Latoschik, Marc Erich O'Brien, James F. Bailenson, Jeremy N. |
| author_facet | Miller, Mark Roman Nair, Vivek Han, Eugy DeVeaux, Cyan Rack, Christian Wang, Rui Huang, Brandon Latoschik, Marc Erich O'Brien, James F. Bailenson, Jeremy N. |
| contents | Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18380 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effect of Duration and Delay on the Identifiability of VR Motion Miller, Mark Roman Nair, Vivek Han, Eugy DeVeaux, Cyan Rack, Christian Wang, Rui Huang, Brandon Latoschik, Marc Erich O'Brien, James F. Bailenson, Jeremy N. Cryptography and Security Human-Computer Interaction Social virtual reality is an emerging medium of communication. In this medium, a user's avatar (virtual representation) is controlled by the tracked motion of the user's headset and hand controllers. This tracked motion is a rich data stream that can leak characteristics of the user or can be effectively matched to previously-identified data to identify a user. To better understand the boundaries of motion data identifiability, we investigate how varying training data duration and train-test delay affects the accuracy at which a machine learning model can correctly classify user motion in a supervised learning task simulating re-identification. The dataset we use has a unique combination of a large number of participants, long duration per session, large number of sessions, and a long time span over which sessions were conducted. We find that training data duration and train-test delay affect identifiability; that minimal train-test delay leads to very high accuracy; and that train-test delay should be controlled in future experiments. |
| title | Effect of Duration and Delay on the Identifiability of VR Motion |
| topic | Cryptography and Security Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.18380 |