  The literature on data sanitization aims to design algorithms that take an
input dataset and produce a privacy-preserving version of it, that captures
some of its statistical properties. In this note we study this question from a
streaming perspective and our goal is to sanitize a data stream. Specifically,
we consider low-memory algorithms that operate on a data stream and produce an
alternative privacy-preserving stream that captures some statistical properties
of the original input stream.
