  Johnson--Lindenstrauss Transforms are powerful tools for reducing the
dimensionality of data while preserving key characteristics of that data, and
they have found use in many fields from machine learning to differential
privacy and more. This note explains what they are; it gives an overview of
their use and their development since they were introduced in the 1980s; and it
provides many references should the reader wish to explore these topics more
deeply.
