Differentially Private Synthetic Data with Private Density Estimation
This work addresses the need for privacy-preserving data analysis in sensitive domains like healthcare and finance, representing an incremental improvement over prior methods.
The paper tackles the problem of generating differentially private synthetic data by adapting an existing optimization-based algorithm, replacing uniform sampling with private density estimation to improve computational guarantees for discrete distributions and create a novel algorithm for continuous distributions.
The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator; this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical tasks.