Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality
Jaybie A. de Guzman, Kanchana Thilakarathna, Aruna Seneviratne

TL;DR
This paper introduces a spatial privacy protection method for AR/MR platforms using conservative data release and spatial generalization, effectively balancing user privacy with data utility in 3D spatial data sharing.
Contribution
It proposes a novel spatial privacy approach with generalized plane release and evaluates its effectiveness against recognition attacks in AR/MR environments.
Findings
Revealing no more than 11 generalized planes prevents adversary location identification in many cases.
Releasing up to 29 generalized planes maintains privacy while improving data utility.
The method effectively balances privacy and utility in 3D spatial data sharing for AR/MR.
Abstract
Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications to place virtual or synthetic objects seemingly "anchored" on to real world objects; in some cases, even allowing interactions between the physical and virtual objects. These functionalities require AR/MR platforms to capture the 3D spatial information with high resolution and frequency; however, these pose unprecedented risks to user privacy. Aside from objects being detected, spatial information also reveals the location of the user with high specificity, e.g. in which part of the house the user is. In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Face recognition and analysis
