  We extend a general approach to evaluating identification risk of synthesized
variables in partially synthetic data. For multiple continuous synthesized
variables, we introduce the use of a radius $r$ in the construction of
identification risk probability of each target record, and illustrate with
working examples. We create the $\texttt{IdentificationRiskCalculation}$ R
package to aid researchers and data disseminators in performing these
identification risks evaluation calculations. We demonstrate our methods
through the R package with applications to a data sample from the Consumer
Expenditure Surveys, and discuss the impacts on risk and data utility of 1) the
choice of radius $r$, 2) the choice of synthesized variables, and 3) the choice
of number of synthetic datasets. We give recommendations for statistical
agencies for synthesizing and evaluating identification risk of continuous
variables.
