  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 data
utility. We designed an adversary that builds up on existing place and shape
recognition methods over 3D data as attackers to which the proposed spatial
privacy approach can be evaluated against. Then, we simulate user movement
within spaces which reveals more of their space as they move around utilizing
3D point clouds collected from Microsoft HoloLens. Results show that revealing
no more than 11 generalized planes--accumulated from successively revealed
spaces with large enough radius, i.e. $r\leq1.0m$--can make an adversary fail
in identifying the spatial location of the user for at least half of the time.
Furthermore, if the accumulated spaces are of smaller radius, i.e. each
successively revealed space is $r\leq 0.5m$, we can release up to 29
generalized planes while enjoying both better data utility and privacy.
