  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.
