research-article

Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data

Authors:

Jaybie Agullo de Guzman,

Aruna Seneviratne,

Kanchana ThilakarathnaAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 14, Pages 1 - 26

https://doi.org/10.1145/3448103

Published: 30 March 2021 Publication History

Abstract

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 evaluation and investigation shows that the cosine similarity-based metric is a good spatial complexity measure of captured 3D spatial maps and can be utilised as an indicator of spatial inference risk.

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Cited By

Tabet SKayssi AElhajj I(2024)Adaptive Mobile Diminished Reality Framework for 3D Visual Privacy2024 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV60512.2024.10620084(1-8)Online publication date: 8-May-2024
https://doi.org/10.1109/ISCV60512.2024.10620084
Liu KZheng TZhou TLiu CLiu FCai Z(2024)The Security and Privacy Concerns on MetaverseComputer Networks and IoT10.1007/978-981-97-1332-5_23(287-305)Online publication date: 3-Apr-2024
https://doi.org/10.1007/978-981-97-1332-5_23
Tabet SKayssi AElhajj I(2023)Mobile Diminished Reality for Preserving 3D Visual Privacy2023 International Conference on Intelligent Metaverse Technologies & Applications (iMETA)10.1109/iMETA59369.2023.10294436(01-07)Online publication date: 18-Sep-2023
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Index Terms

Unravelling Spatial Privacy Risks of Mobile Mixed Reality Data
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Mixed / augmented reality
2. Security and privacy
  1. Database and storage security
    1. Information accountability and usage control
  2. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

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Published: 30 March 2021

Published in IMWUT Volume 5, Issue 1

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Cited By

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https://doi.org/10.1109/ISCV60512.2024.10620084
Liu KZheng TZhou TLiu CLiu FCai Z(2024)The Security and Privacy Concerns on MetaverseComputer Networks and IoT10.1007/978-981-97-1332-5_23(287-305)Online publication date: 3-Apr-2024
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https://doi.org/10.1109/iMETA59369.2023.10294436
Braud TLee LAlhilal AFernández CHui P(2023)DiOS—An Extended Reality Operating System for the MetaverseIEEE MultiMedia10.1109/MMUL.2022.321135130:2(70-80)Online publication date: 1-Apr-2023
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Guzman JThilakarathna KSeneviratne A(2023)Privacy and Security Issues and Solutions for Mixed Reality ApplicationsSpringer Handbook of Augmented Reality10.1007/978-3-030-67822-7_7(157-183)Online publication date: 1-Jan-2023
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