  The proliferation of big data has brought an urgent demand for
privacy-preserving data publishing. Traditional solutions to this demand have
limitations on effectively balancing the tradeoff between privacy and utility
of the released data. Thus, the database community and machine learning
community have recently studied a new problem of relational data synthesis
using generative adversarial networks (GAN) and proposed various algorithms.
However, these algorithms are not compared under the same framework and thus it
is hard for practitioners to understand GAN's benefits and limitations. To
bridge the gaps, we conduct so far the most comprehensive experimental study
that investigates applying GAN to relational data synthesis. We introduce a
unified GAN-based framework and define a space of design solutions for each
component in the framework, including neural network architectures and training
strategies. We conduct extensive experiments to explore the design space and
compare with traditional data synthesis approaches. Through extensive
experiments, we find that GAN is very promising for relational data synthesis,
and provide guidance for selecting appropriate design solutions. We also point
out limitations of GAN and identify future research directions.
