Balanced Iteration Subsampling achieves stronger privacy amplification than Poisson subsampling in DP-SGD by eliminating participation variance while keeping uniform marginal participation.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
DP-GD achieves minimax optimal non-asymptotic risk O(γ + γ²/ρ²) for well-conditioned high-dimensional data and power-law scaling for ill-conditioned power-law spectra, with the exponent depending on the privacy parameter ρ.
citing papers explorer
-
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
Balanced Iteration Subsampling achieves stronger privacy amplification than Poisson subsampling in DP-SGD by eliminating participation variance while keeping uniform marginal participation.
-
High-Dimensional Private Linear Regression with Optimal Rates
DP-GD achieves minimax optimal non-asymptotic risk O(γ + γ²/ρ²) for well-conditioned high-dimensional data and power-law scaling for ill-conditioned power-law spectra, with the exponent depending on the privacy parameter ρ.
- Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD