  The need to analyze sensitive data, such as medical records or financial
data, has created a critical research challenge in recent years. In this paper,
we adopt the framework of differential privacy, and explore mechanisms for
generating an entire dataset which accurately captures characteristics of the
original data. We build upon the work of Boedihardjo et al, which laid the
foundations for a new optimization-based algorithm for generating private
synthetic data. Importantly, we adapt their algorithm by replacing a uniform
sampling step with a private distribution estimator; this allows us to obtain
better computational guarantees for discrete distributions, and develop a novel
algorithm suitable for continuous distributions. We also explore applications
of our work to several statistical tasks.
