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cs.CR2026
Tight End-to-End Accounting for Differentially Private Synthetic Data
Georgi Ganev, Vadym Doroshenko, Arun Ganesh +3
Differentially private (DP) pipelines often combine heterogeneous mechanisms, yet their guarantees are best analyzed using different notions of privacy (e.g., bounded range, zCDP,…
cs.CR2026
Better Batches, Better Privacy: Random Allocation for DP-BandMF
Ryan McKenna, Arun Ganesh
The current state of the art for batch selection and noise addition in differentially private training is a combination of DP-BandMF (informally, DP-SGD where the noise added in tw…
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…