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

Learning to Censor by Noisy Sampling