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Can I read Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data on EtoBox?
Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data by Guzman, Jaybie Agullo de (author);Seneviratne, Aruna (author);Thilakarathna, Kanchana (author) is a Computer Science article available to read on EtoBox.
What is Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data about?
Previously, 3D data---particularly, spatial data---have primarily been utilized in the field of geo-spatial analyses, or robot navigation (e.g. self-automated cars) as 3D representations of geographical or terrain data (usually extracted from lidar). Now, with the increasing user adoption of augmented, mixed, and virtual reality (AR/MR/VR; we collectively refer to as MR) technology on user mobile devices, spatial data has become more ubiquitous. However, this ubiquity also opens up a new threat vector for adversaries: aside from the traditional forms of mobile media such as images and video, spatial data poses additional and, potentially, latent risks to users of AR/MR/VR. Thus, in this work, we analyse MR spatial data using various spatial complexity metrics---including a cosine similarity-based, and a Euclidean distance-based metric---as heuristic or empirical measures that can signify the inference risk a captured space has. To demonstrate the risk, we utilise 3D shape recognition and classification algorithms for spatial inference attacks over various 3D spatial data captured using mobile MR platforms: i.e. Microsoft HoloLens, and Android with Google ARCore. Our experimental ev
Who reads Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Guzman, Jaybie Agullo de (author);Seneviratne, Aruna (author);Thilakarathna, Kanchana (author)
- Publisher
- Association for Computing Machinery (ACM)
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)