Generalizing DP-SGD with Shuffling and Batch Clipping
This work provides a more flexible and efficient approach to differential privacy in machine learning, though it is incremental as it builds upon existing DP-SGD methods.
The authors tackled the limitation of DP-SGD by developing a general differential privacy framework that supports various first-order optimizers and sampling techniques like shuffling, achieving a DP dependency of √(gE) for groups of size g over E epochs.
Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the $f$-DP approach and introduces a new proof technique which allows us to derive simple closed form expressions and to also analyse group privacy. In particular, for $E$ epochs work and groups of size $g$, we show a $\sqrt{g E}$ DP dependency for batch clipping with shuffling.