With the rapid development of deep learning in the field of action recognition, there is a growing concern about the invasion of privacy due to the need for large amounts of data for training and the collection of personal data. To alleviate these concerns while ensuring the advancement of research on action recognition, research on Privacy-preserving action recognition (PPAR) has emerged. PPAR requires the model to remove the private information contained in videos and recognize the action performed in the private information-free videos. Many efforts have been made to solve this problem from different aspects. In this work, we first outline the common framework for training PPAR models and formulate the goals of PPAR. Then, we review the commonly used datasets in PPAR research and detailedly explain the different definitions and evaluations of privacy protection in these datasets. The demand for PPAR datasets is also discussed. Furthermore, we categorize existing PPAR methods according to the way of removing private information. To inspire insights into potential future research directions, we comprehensively review each category of these existing PPAR methods for addressing PPAR. An objective analysis of existing methods’ astonishing improvements as well as their inevitable drawback is provided.