论文揭示了点云加密方案的安全漏洞，2X加密模式下机器学习仍能重建加密数据。
研究人员评估了选择性坐标加密对机器学习重建攻击的鲁棒性。实验使用PointNet和随机森林模型，在X和2X两种加密粒度下测试。结果显示，完全加密X坐标仍具挑战性，但2X方案会通过相邻坐标泄露足够信息，实现准确重建。
Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds
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.