The recent, remarkable growth of machine learning has led to intense interest in
the privacy of the data on which machine learning relies, and to new techniques for
preserving privacy. However, older ideas about privacy may well remain valid and
useful. This note reviews two recent works on privacy in the light of the wisdom of
some of the early literature, in particular the principles distilled by Saltzer and
Schroeder in the 1970s.