  Synthetic data has gained significant momentum thanks to sophisticated
machine learning tools that enable the synthesis of high-dimensional datasets.
However, many generation techniques do not give the data controller control
over what statistical patterns are captured, leading to concerns over privacy
protection. While synthetic records are not linked to a particular real-world
individual, they can reveal information about users indirectly which may be
unacceptable for data owners. There is thus a need to empirically verify the
privacy of synthetic data -- a particularly challenging task in
high-dimensional data. In this paper we present a general framework for
synthetic data generation that gives data controllers full control over which
statistical properties the synthetic data ought to preserve, what exact
information loss is acceptable, and how to quantify it. The benefits of the
approach are that (1) one can generate synthetic data that results in high
utility for a given task, while (2) empirically validating that only statistics
considered safe by the data curator are used to generate the data. We thus show
the potential for synthetic data to be an effective means of releasing
confidential data safely, while retaining useful information for analysts.
