1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.CR2026★ 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.LG2024
Private prediction for large-scale synthetic text generation
Kareem Amin, Alex Bie, Weiwei Kong +5
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only…