Generalizing DP-SGD with Shuffling and Batch Clipping

• URL: http://arxiv.org/abs/2212.05796v3
• Date: Tue, 25 Jul 2023 16:57:52 GMT
• Title: Generalizing DP-SGD with Shuffling and Batch Clipping
• Authors: Marten van Dijk, Phuong Ha Nguyen, Toan N. Nguyen and Lam M. Nguyen
• Abstract summary: 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 summings. We show a $sqrtg E$ DP dependency for batch clipping with shuffling.
• Score: 21.55827140532476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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