The U.S. Census Bureau announced, via its Scientific Advisory Committee, that it would protect the publications from the 2020 Census of Population and Housing using differential privacy. The decennial census is the constitutionally mandated enumeration of the population used to reapportion the House of Representatives and redraw every legislative district in the country. The Census Bureau conducted internal research confirming that the statistical disclosure limitation systems used for the 2000 and 2010 Censuses had serious vulnerabilities that were exposed by the Dinur and Nissim (2003) database reconstruction theorem. We designed a differentially private publication system that directly addressed these vulnerabilities while preserving the fitness for use of the core statistical products. This is the largest-scale central differential privacy implementation ever undertaken. Designing efficient algorithms lies in the domain of computer science. Choosing the actual privacy-loss parameters to implement lies in the domain of economics. Our algorithms efficiently distribute the noise injected by differential privacy for any privacy-loss parameter in order to insure fitness-for-use of the 2020 Census statistics. The Census Bureau’s Data Stewardship Executive Policy Committee selects the privacy-loss parameters after reviewing graphical summaries of the accuracy versus privacy-loss tradeoff. These decisions are made before the algorithms are run on the actual 2020 Census data.