With the era of big data and the development of artificial intelligence, Federated learning (FL) emerges as a distributed machine learning approach. It allows multiple participants to train a global model collaboratively while keeping each of their training datasets in local devices. FL is created to break up data silos and preserve the privacy and security of data. However, there are still a large number of privacy risks during data exchange steps, where local data is threatened not only by model users as in centralized training but also by any dishonest participants. It is necessary to study technologies to achieve rigorous privacy-preserving approaches. The research progress and trend of privacy-preserving techniques for FL are surveyed in this paper. At first, the architecture and type of FL are introduced, then privacy risks and attacks are illustrated, including reconstruction and inference strategies. According to the mechanism of privacy preservation, the main privacy protection technologies are introduced. By applying these technologies, privacy defense strategies are presented and they are abstracted as 3 levels: local, central, local & central. Challenges and future directions of privacy-preserving in federated learning are discussed at last.