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Differentially Private Synthetic Data with Private Density Estimation
May 9, 2024, 4:41 a.m. | Nikolija Bojkovic, Po-Ling Loh
cs.LG updates on arXiv.org arxiv.org
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 …
abstract analyze arxiv challenge cs.cr cs.it cs.lg data dataset differential differential privacy explore financial framework math.it math.st medical medical records paper privacy records research stat.ml stat.th synthetic synthetic data type
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