  This paper describes privacy-preserving approaches for the statistical
analysis. It describes motivations for privacy-preserving approaches for the
statistical analysis of sensitive data, presents examples of use cases where
such methods may apply and describes relevant technical capabilities to assure
privacy preservation while still allowing analysis of sensitive data. Our focus
is on methods that enable protecting privacy of data while it is being
processed, not only while it is at rest on a system or in transit between
systems. The information in this document is intended for use by statisticians
and data scientists, data curators and architects, IT specialists, and security
and information assurance specialists, so we explicitly avoid cryptographic
technical details of the technologies we describe.
