Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. In this research, we propose the first unlearnable approach for 3D point clouds via \underline{U}nlearnable \underline{M}ulti-\underline{T}ransformations (UMT), which involves a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples. Additionally, we observe even authorized users struggle to extract and learn the knowledge of 3D unlearnable data, an aspect overlooked in most existing 2D unlearnable literature. In response, we propose a data restoration scheme that enables authorized-only training for unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework.