发表机构
University of Massachusetts Amherst; Tennessee Technological University（马萨诸塞大学阿默斯特分校; 田纳西理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对选择性坐标加密框架，利用PointNet和随机森林模型开展重建攻击实验，发现加密粒度影响其安全性，2X方案可通过相邻坐标泄露信息实现准确重建。
AI 中文摘要
基于点云表示的体积视频可实现沉浸式虚拟现实与增强现实应用，但对高效且安全的内容传输提出了重大挑战。现有研究提出了一种针对点云的选择性坐标加密框架，该框架仅对部分坐标进行加密，在降低计算成本的同时使未授权内容的视觉质量下降。然而，目前尚不清楚剩余未加密信息是否足以实现内容重建。在本文中，我们评估选择性坐标加密抵御基于机器学习的重建攻击的鲁棒性，考虑攻击者在无法解密的情况下，通过利用未加密数据中的空间与几何相关性，尝试恢复已加密坐标。我们在两种加密粒度下评估PointNet与随机森林（Random Forest）模型：加密所有X坐标的\texttt{X}方案，以及每隔一个X坐标加密的\texttt{2X}方案。结果显示，重建完全加密的X坐标仍具挑战性，而\texttt{2X}方案通过相邻坐标泄露了足够信息，可实现准确重建。这些发现表明，选择性坐标加密的安全性高度依赖于加密粒度。
英文摘要
Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challenges for efficient and secure content delivery. Prior work proposed a selective coordinate encryption framework for point clouds that encrypts only a subset of coordinates, reducing computational costs while visually degrading unauthorized content. However, it remains unclear whether the remaining unencrypted information is sufficient to enable content reconstruction. In this paper, we evaluate the robustness of selective coordinate encryption against machine learning-based reconstruction attacks. We consider an attacker with access to selectively encrypted point clouds attempting to recover encrypted coordinates without decryption by exploiting spatial and geometric correlations in the unencrypted data. We evaluate PointNet and Random Forest models under two encryption granularities: \texttt{X}, where all $X$ coordinates are encrypted, and \texttt{2X}, where every second $X$ coordinate is encrypted. Our results show that reconstructing fully encrypted $X$ coordinates remains challenging, whereas the \texttt{2X} scheme leaks sufficient information through neighboring coordinates to enable accurate reconstruction. These findings demonstrate that the security of selective coordinate encryption depends strongly on encryption granularity.
Comments6 pages, 4 figure, accepted at XR Security workshop 2026
