Title: Privacy-Preserving Vision Transformer on Permutation-Encrypted Images. Abstract: Massive human-related data is collected to train neural networks for computer vision tasks. Potential incidents, such as data leakages, expose significant privacy risks to applications. In this paper, we propose an efficient privacy-preserving learning paradigm, where images are first encrypted via one of the two encryption strategies: (1) random shuffling to a set of equally-sized patches and (2) mixing-up sub-patches of the images. Then, a permutation-equivariant vision transformer is designed to learn on the encrypted images for vision tasks, including image classification and object detection. Extensive experiments on ImageNet and COCO show that the proposed paradigm achieves comparable accuracy with the competitive methods. Moreover, decrypting the encrypted images is solving an NP-hard jigsaw puzzle or an ill-posed inverse problem, which is empirically shown intractable to be recovered by the powerful vision transformer-based attackers. We thus show that the proposed paradigm can destroy human-recognizable contents while preserving machine-learnable information. Code will be released publicly.