Related papers: Differentially Private Synthetic Data with Private Density Estimation

Differentially Private Synthetic Data with Private Density Estimation

URL: http://arxiv.org/abs/2405.04554v1
Date: Mon, 6 May 2024 14:06:12 GMT
Title: Differentially Private Synthetic Data with Private Density Estimation
Authors: Nikolija Bojkovic, Po-Ling Loh,
Abstract summary: We adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data.
Score: 2.209921757303168
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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