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User configurable 3D object regeneration for spatial privacy
Cryptography and Security · Submitted 10 Aug 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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 $0.17<l<0.25$.
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The abstract, sentence by sentence
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 $0.17<l<0.25$.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | 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. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s2 | 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. | background 0.90 | 0.06 | 0.03 | asserted 0.80 |
| s3 | Thus, in this work, we demonstrate how we can leverage 3D object regeneration for preserving privacy of 3D point clouds. | contribution 1.00 | 0.87 | 0.29 | asserted 1.00 |
| s4 | That is, we employ an intermediary layer of protection to transform the 3D point cloud before providing it to the third-party applications. | method 0.89 | 0.84 | 0.08 | asserted 0.99 |
| s5 | 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. | method 0.99 | 0.58 | 0.04 | asserted 1.00 |
| s6 | 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. | method 0.99 | 0.91 | 0.03 | asserted 0.93 |
| s7 | 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. | method 0.88 | 0.85 | 0.05 | asserted 0.96 |
| s8 | 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. | result 1.00 | 0.95 | 0.25 | asserted 0.99 |
| s9 | 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 $0.17<l<0.25$. | result 0.99 | 0.95 | 0.34 | asserted 0.72 |
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The paper
Original abstract
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 $0.17<l<0.25$.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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Some works proposed an encryption scheme based on chaotic mapping [ref] or optical chaotic encryption [ref] , and a 3D object reconstruction technique was introduced [ref] , both achieving privacy protection for individual 3D point cloud data.
Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need · Protecting 3D Point Cloud Data Privacy · 0.93
What it cites · 1 sentence without a confident label
Builds on 1 paper
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To formally define the problem and the privacy and utility metrics used in this work, we develop upon the formalization introduced in [ref] and [ref] .
Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality · Adversary models · 0.92
Uses data or values from 2 papers
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For this work, we created a custom dataset by selecting a subset of the dataset introduced by [ref] where 3D CAD models of ShapeNetCore [ref] were sampled with (area) uniform sampling.
ShapeNet: An Information-Rich 3D Model Repository · Dataset · 0.99
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For this work, we created a custom dataset by selecting a subset of the dataset introduced by [ref] where 3D CAD models of ShapeNetCore [ref] were sampled with (area) uniform sampling.
Learning Representations and Generative Models for 3D Point Clouds · Dataset · 0.99
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