Privacy computing refers to the use of technology to ensure that data is usable by participants without being visible, allowing the data to be circulated and shared without compromising security and privacy. This project is written in Python and supports various mainstream privacy computing technologies including MPC (Multi-Party Computation), FL (Federated Learning), TEE (Trusted Execution Environment), HE (Homomorphic Encryption), and DP (Differential Privacy).