5 papers
Privacy Amplification for BandMF via -Min-Sep Subsampling
Andy Dong, Arun Ganesh
We study privacy amplification for BandMF, i.e., DP-SGD with correlated noise across iterations via a banded correlation matrix. We propose -min-sep subsampling, a new subsampli…
JAX-Privacy: A library for differentially private machine learning
Ryan McKenna, Galen Andrew, Borja Balle +6
JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usabili…
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.
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…
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…