  When sharing data among researchers or releasing data for public use, there
is a risk of exposing sensitive information of individuals in the data set.
Data synthesis (DS) is a statistical disclosure limitation technique for
releasing synthetic data sets with pseudo individual records. Traditional DS
techniques often rely on strong assumptions of a data intruder's behaviors and
background knowledge to assess disclosure risk. Differential privacy (DP)
formulates a theoretical approach for a strong and robust privacy guarantee in
data release without having to model intruders' behaviors. Efforts have been
made aiming to incorporate the DP concept in the DS process. In this paper, we
examine current DIfferentially Private Data Synthesis (DIPS) techniques for
releasing individual-level surrogate data for the original data, compare the
techniques conceptually, and evaluate the statistical utility and inferential
properties of the synthetic data via each DIPS technique through extensive
simulation studies. Our work sheds light on the practical feasibility and
utility of the various DIPS approaches, and suggests future research directions
for DIPS.
