Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要