  Point clouds are an increasingly ubiquitous input modality and the raw signal
can be efficiently processed with recent progress in deep learning. This signal
may, often inadvertently, capture sensitive information that can leak semantic
and geometric properties of the scene which the data owner does not want to
share. The goal of this work is to protect sensitive information when learning
from point clouds; by censoring the sensitive information before the point
cloud is released for downstream tasks. Specifically, we focus on preserving
utility for perception tasks while mitigating attribute leakage attacks. The
key motivating insight is to leverage the localized saliency of perception
tasks on point clouds to provide good privacy-utility trade-offs. We realize
this through a mechanism called Censoring by Noisy Sampling (CBNS), which is
composed of two modules: i) Invariant Sampler: a differentiable point-cloud
sampler which learns to remove points invariant to utility and ii) Noisy
Distorter: which learns to distort sampled points to decouple the sensitive
information from utility, and mitigate privacy leakage. We validate the
effectiveness of CBNS through extensive comparisons with state-of-the-art
baselines and sensitivity analyses of key design choices. Results show that
CBNS achieves superior privacy-utility trade-offs on multiple datasets.
