  Environmental understanding capability of $\textit{augmented}$ (AR) and
$\textit{mixed reality}$ (MR) devices are continuously improving through
advances in sensing, computer vision, and machine learning. Various AR/MR
applications demonstrate such capabilities i.e. scanning a space using a
handheld or head mounted device and capturing a digital representation of the
space that are accurate copies of the real space. However, these capabilities
impose privacy risks to users: personally identifiable information can leak
from captured 3D maps of the sensitive spaces and/or captured sensitive objects
within the mapped space. Thus, in this work, we demonstrate how we can leverage
3D object regeneration for preserving privacy of 3D point clouds. That is, we
employ an intermediary layer of protection to transform the 3D point cloud
before providing it to the third-party applications. Specifically, we use an
existing adversarial autoencoder to generate copies of 3D objects where the
likeness of the copies from the original can be varied. To test the viability
and performance of this method as a privacy preserving mechanism, we use a 3D
classifier to classify and identify these transformed point clouds i.e. perform
$\textit{super}$-class and $\textit{intra}$-class classification. To measure
the performance of the proposed privacy framework, we define privacy,
$\Pi\in[0,1]$, and utility metrics, $Q\in[0,1]$, which are desired to be
maximized. Experimental evaluation shows that the privacy framework can indeed
variably effect the privacy of a 3D object by varying the privilege level
$l\in[0,1]$ i.e. if a low $l<0.17$ is maintained, $\Pi_1,\Pi_2>0.4$ is ensured
where $\Pi_1,\Pi_2$ are super- and intra-class privacy. Lastly, the privacy
framework can ensure relatively high intra-class privacy and utility i.e.
$\Pi_2>0.63$ and $Q>0.70$, if the privilege level is kept within the range of
$0.17<l<0.25$.
