  We propose a general, flexible, and scalable framework dpart, an open source
Python library for differentially private synthetic data generation. Central to
the approach is autoregressive modelling -- breaking the joint data
distribution to a sequence of lower-dimensional conditional distributions,
captured by various methods such as machine learning models (logistic/linear
regression, decision trees, etc.), simple histogram counts, or custom
techniques. The library has been created with a view to serve as a quick and
accessible baseline as well as to accommodate a wide audience of users, from
those making their first steps in synthetic data generation, to more
experienced ones with domain expertise who can configure different aspects of
the modelling and contribute new methods/mechanisms. Specific instances of
dpart include Independent, an optimized version of PrivBayes, and a newly
proposed model, dp-synthpop.
  Code: https://github.com/hazy/dpart
