Scalable DP-SGD: Shuffling vs. Poisson Subsampling

• URL: http://arxiv.org/abs/2411.04205v1
• Date: Wed, 06 Nov 2024 19:06:16 GMT
• Title: Scalable DP-SGD: Shuffling vs. Poisson Subsampling
• Authors: Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang,
• Abstract summary: We provide new lower bounds on the privacy guarantee of the multi-epoch Adaptive Linear Queries (ABLQ) mechanism with shuffled batch sampling. We show substantial gaps when compared to Poisson subsampling; prior analysis was limited to a single epoch. We introduce a practical approach to implement Poisson subsampling at scale using massively parallel computation.
• Score: 61.19794019914523
• License:
• Abstract: We provide new lower bounds on the privacy guarantee of the multi-epoch Adaptive Batch Linear Queries (ABLQ) mechanism with shuffled batch sampling, demonstrating substantial gaps when compared to Poisson subsampling; prior analysis was limited to a single epoch. Since the privacy analysis of Differentially Private Stochastic Gradient Descent (DP-SGD) is obtained by analyzing the ABLQ mechanism, this brings into serious question the common practice of implementing shuffling-based DP-SGD, but reporting privacy parameters as if Poisson subsampling was used. To understand the impact of this gap on the utility of trained machine learning models, we introduce a practical approach to implement Poisson subsampling at scale using massively parallel computation, and efficiently train models with the same. We compare the utility of models trained with Poisson-subsampling-based DP-SGD, and the optimistic estimates of utility when using shuffling, via our new lower bounds on the privacy guarantee of ABLQ with shuffling.

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