  The Census TopDown Algorithm (TDA) is a disclosure avoidance system using
differential privacy for privacy-loss accounting. The algorithm ingests the
final, edited version of the 2020 Census data and the final tabulation
geographic definitions. The algorithm then creates noisy versions of key
queries on the data, referred to as measurements, using zero-Concentrated
Differential Privacy. Another key aspect of the TDA are invariants, statistics
that the Census Bureau has determined, as matter of policy, to exclude from the
privacy-loss accounting. The TDA post-processes the measurements together with
the invariants to produce a Microdata Detail File (MDF) that contains one
record for each person and one record for each housing unit enumerated in the
2020 Census. The MDF is passed to the 2020 Census tabulation system to produce
the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper
describes the mathematics and testing of the TDA for this purpose.
