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arXiv · 2405.15913

Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML

Ryan McKenna
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Abstract

Correlated noise mechanisms such as DP Matrix Factorization (DP-MF) have proven to be effective alternatives to DP-SGD in large-epsilon few-epoch training regimes. Significant work has been done to find the best correlated noise strategies, and the current state-of-the-art approach is DP-BandMF, which optimally balances the benefits of privacy amplification and noise correlation. Despite it's utility advantages, severe scalability limitations prevent this mechanism from handling large-scale training scenarios where the number of training iterations may exceed $10^4$ and the number of model parameters may exceed $10^7$. In this work, we present techniques to scale up DP-BandMF along these two dimensions, significantly extending it's reach and enabling it to handle settings with virtually any number of model parameters and training iterations, with negligible utility degradation.

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Ryan McKenna. 2024-05-24. Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML. https://arxiv.org/abs/2405.15913

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