2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CR2025
Tighter Privacy Analysis for Truncated Poisson Sampling
Arun Ganesh
We give a new privacy amplification analysis for truncated Poisson sampling, a Poisson sampling variant that truncates a batch if it exceeds a given maximum batch size.
cs.LG2025★ 2 cited
Correlated Noise Mechanisms for Differentially Private Learning
Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo +9
This monograph explores the design and analysis of correlated noise mechanisms for differential privacy (DP), focusing on their application to private training of AI and machine le…
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…