Feb. 8, 2024, 5:10 a.m. | Dingfan Chen Marie Oestreich Tejumade Afonja Raouf Kerkouche Matthias Becker Mario Fritz

cs.CR updates on arXiv.org arxiv.org

Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data. We conduct a comprehensive analysis of five representative DP generation …

applications basic benchmarking cs.cr cs.lg data datasets differential privacy distributions gene generative generative models literature metrics privacy private report results simple synthetic synthetic data

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