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.LG2025
It's My Data Too: Private ML for Datasets with Multi-User Training Examples
Arun Ganesh, Ryan McKenna, Brendan McMahan +2
We initiate a study of algorithms for model training with user-level differential privacy (DP), where each example may be attributed to multiple users, which we call the multi-attr…