4 citations · 6 across the 8 of their papers we have counts for
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cs.CR2025★ 1 cited
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
cs.CR2025★ 4 cited
Benchmarking Differentially Private Tabular Data Synthesis
Kai Chen, Xiaochen Li, Chen Gong +2
Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The em…