With the popularization of cloud computing and big data， increasing user privacy data was updated for cloud computing and processing. However， as privacy data was stored and managed by untrusted third parties， user private data faces the risk of privacy leakage， thereby affecting the safety of citizens’ lives and property， and even national security. In recent years， several privacy preserving techniques based on cryptographic algorithms， such as secure multi-party computation， Homomorphic Encryption （HE）， and federated learning， solve the security issues in the transmission and computation process of private data， thereby achieving “usable but invisible” of private data. However， these schemes have not been widely deployed and applied due to their computational and communication complexity. At the same time， much research devotes to use Trusted Execution Environment （TEE） to reduce the computational and communication complexity of privacy preserving techniques while ensuring security of these techniques. TEEs create execution environments that can be trusted with hardware assistance， and ensure the confidentiality， integrity， and availability of privacy data and code in the environment. Therefore， start from the research combining privacy computing and TEEs， the review was performed. Firstly， the system architecture and hardware support of TEEs to protect the user data privacy were analyzed comprehensively. Then， the advantages and disadvantages of the existing TEE architectures were compared. Finally， combined with the latest developments in industry and academia， the future development trends of the cross-research field of privacy computing and TEEs were discussed.