  Differential privacy is an information theoretic constraint on algorithms and
code. It provides quantification of privacy leakage and formal privacy
guarantees that are currently considered the gold standard in privacy
protections. In this paper we provide an initial set of "best practices" for
developing differentially private platforms, techniques for unit testing that
are specific to differential privacy, guidelines for checking if differential
privacy is being applied correctly in an application, and recommendations for
parameter settings. The genesis of this paper was an initiative by Facebook and
Social Science One to provide social science researchers with programmatic
access to a URL-shares dataset. In order to maximize the utility of the data
for research while protecting privacy, researchers should access the data
through an interactive platform that supports differential privacy.
  The intention of this paper is to provide guidelines and recommendations that
can generally be re-used in a wide variety of systems. For this reason, no
specific platforms will be named, except for systems whose details and theory
appear in academic papers.
