3 papers
cs.CR2026
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…
cs.LG2026
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
Andy Dong, Ayfer Ãzgür
Poisson subsampling is the default sampling scheme in differentially private machine learning, largely because its unstructured randomness yields tractable privacy amplification an…
cs.LG2025
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
Andy Dong, Wei-Ning Chen, Ayfer Ozgur
We study how inherent randomness in the training process -- where each sample (or client in federated learning) contributes only to a randomly selected portion of training -- can b…