  This paper explains how the synthpop package for R has been extended to
include functions to calculate measures of identity and attribute disclosure
risk for synthetic data that measure risks for the records used to create the
synthetic data. The basic function, disclosure, calculates identity disclosure
for a set of quasi-identifiers (keys) and attribute disclosure for one variable
specified as a target from the same set of keys. The second function,
disclosure.summary, is a wrapper for the first and presents summary results for
a set of targets. This short paper explains the measures of disclosure risk and
documents how they are calculated. We recommend two measures: $RepU$
(replicated uniques) for identity disclosure and $DiSCO$ (Disclosive in
Synthetic Correct Original) for attribute disclosure. Both are expressed a \%
of the original records and each can be compared to similar measures calculated
from the original data. Experience with using the functions on real data found
that some apparent disclosures could be identified as coming from relationships
in the data that would be expected to be known to anyone familiar with its
features. We flag cases when this seems to have occurred and provide means of
excluding them.
