Secure multi-party computation enables a group of parties to compute a function while jointly keeping their private inputs secret. The term "secure" indicates the latter property where the private inputs used for computation are kept secret from all other parties. A significant benefit of using secure multi-party computation is that many constructed protocols are information-theoretically secure, avoiding many problems using cryptographic hardness assumptions. Some notable use cases are secure auctions, privacy-preserving network security monitoring, spam filtering on encrypted emails, and secure machine learning. Secure multi-party computation can be used to secure and enable privacy-preserving applications from privacy-preserving network security to secure machine learning.