  We present three new algorithms for constructing differentially private
synthetic data---a sanitized version of a sensitive dataset that approximately
preserves the answers to a large collection of statistical queries. All three
algorithms are \emph{oracle-efficient} in the sense that they are
computationally efficient when given access to an optimization oracle. Such an
oracle can be implemented using many existing (non-private) optimization tools
such as sophisticated integer program solvers. While the accuracy of the
synthetic data is contingent on the oracle's optimization performance, the
algorithms satisfy differential privacy even in the worst case. For all three
algorithms, we provide theoretical guarantees for both accuracy and privacy.
Through empirical evaluation, we demonstrate that our methods scale well with
both the dimensionality of the data and the number of queries. Compared to the
state-of-the-art method High-Dimensional Matrix Mechanism \cite{McKennaMHM18},
our algorithms provide better accuracy in the large workload and high privacy
regime (corresponding to low privacy loss $\varepsilon$).
