Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

• URL: http://arxiv.org/abs/2403.07797v1
• Date: Tue, 12 Mar 2024 16:34:07 GMT
• Title: Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
• Authors: Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
• Abstract summary: We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. 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.
• Score: 13.56146208014469
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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