Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

arXiv:2403.07797v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ai cs.lg data however information pre-training public public data synthetic synthetic data training type