Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
<|reference_start|>Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data: 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.<|reference_end|>
@article{fuentes2024joint,
    title={Joint Selection: Adaptively Incorporating Public Information for Private
  Synthetic Data},
    author={Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel
  Sheldon},
    journal={arXiv preprint arXiv:2403.07797},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07797},
    primaryClass={cs.LG cs.AI}
}