Privacy-Preserving Computation refers to techniques that allow data to be processed and analyzed without exposing sensitive information. This approach ensures that individual data points remain confidential while still enabling valuable insights to be derived from the overall dataset. Common methods include homomorphic encryption, which allows computations on encrypted data, and secure multi-party computation, where multiple parties can jointly compute a function without revealing their private inputs. These techniques are essential in fields like healthcare and finance, where protecting personal information is crucial while still needing to perform data analysis.