Joint Selection: Adaptively Incorporating Public Information for Private
  Synthetic Data
Miguel Fuentes Brett Mullins Ryan McKenna Gerome Miklau Daniel Sheldon
Mechanisms for generating differentially private synthetic data based on
marginals and graphical models have been successful in a wide range of
settings. However, one limitation of these methods is their inability to
incorporate public data. Initializing a data generating model by pre-training
on public data has shown to improve the quality of synthetic data, but this
technique is not applicable when model structure is not determined a priori. We
develop the mechanism jam-pgm, which expands the adaptive measurements
framework to jointly select between measuring public data and private data.
This technique allows for public data to be included in a graphical-model-based
mechanism. We show that jam-pgm is able to outperform both publicly assisted
and non publicly assisted synthetic data generation mechanisms even when the
public data distribution is biased.