New Research Suggests Privacy in the Metaverse May Be Impossible

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A new paper from the University of California, Berkeley reveals that privacy may be impossible in the metaverse without innovative new safeguards to protect users.

Led by graduate researcher Vivek Nair, the recently published study was conducted at the Center for Responsible Decentralized Intelligence (RDI) and involved the largest dataset of virtual reality (VR) user interactions ever analyzed for privacy risks.< /p>

What makes the results so surprising is how little data is needed to uniquely identify a user in the metaverse, which potentially eliminates any possibility of true anonymity in virtual worlds.

Simple motion data not so simplistic

Behind the scenes, most researchers and policy makers who study metaverse privacy focus on the numerous cameras and microphones of modern VR headsets that capture detailed information about facial features, voice qualities, and movements of the user's eyes, as well as ambient information about the user's home or environment. office.

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Some researchers are even concerned about emerging technologies such as EEG sensors that can detect single brain activity through the scalp. While these rich data streams pose serious privacy risks in the metaverse, disabling them all may not provide anonymity.

That's because the most basic stream of data needed to interact with a virtual world (simple motion data) can be enough to uniquely identify a user within a large population.

And by "simple motion data" I mean the three most basic data points tracked by VR systems: one point on the user's head and one on each hand. Researchers often refer to it as "telemetry data" and represents the minimum data set required to allow a user to interact naturally in a virtual environment.

Unique identification in seconds

This brings me to Berkeley's new study, "Unique Identification of Over 50,000 Virtual Reality Users from Head and Hand Movement Data". The research analyzed over 2.5 million (fully anonymized) VR data records from over 50,000 players of the popular Beat Saber app and found that individual users could be uniquely identified with greater accuracy. by 94% using just 100 seconds of motion data.

Even more surprisingly, half of all users could be uniquely identified with just 2 seconds of motion data. Achieving this level of precision required innovative AI techniques, but again, the data used was extremely sparse: only three spatial points for each user tracked over time.

< /p> A user playing Beat Saber in the metaverse Courtesy of Vivek Nair, U.C. Berkeley

In other words, every time a user puts a...