Privacy enhancing computation (PEC) refers to a set of technologies and methods designed to enable data processing and analysis while preserving the privacy and confidentiality of the information involved. PEC allows sensitive data to be used in computations without exposing it to unauthorized parties or compromising its security. Techniques under PEC include methods like homomorphic encryption, secure multi-party computation (SMPC), and differential privacy, which ensure that data can be processed, shared, or analyzed in a secure manner, even in untrusted environments. The goal of PEC is to strike a balance between utilizing valuable data insights and maintaining strict privacy standards, making it possible to perform computations on sensitive data without revealing the underlying information.