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arXiv · 2311.08357

Sparsity-Preserving Differentially Private Training of Large Embedding Models

Badih Ghazi·Yangsibo Huang·Pritish Kamath·Ravi Kumar·Pasin Manurangsi·Amer Sinha·Chiyuan Zhang
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Abstract

As the use of large embedding models in recommendation systems and language applications increases, concerns over user data privacy have also risen. DP-SGD, a training algorithm that combines differential privacy with stochastic gradient descent, has been the workhorse in protecting user privacy without compromising model accuracy by much. However, applying DP-SGD naively to embedding models can destroy gradient sparsity, leading to reduced training efficiency. To address this issue, we present two new algorithms, DP-FEST and DP-AdaFEST, that preserve gradient sparsity during private training of large embedding models. Our algorithms achieve substantial reductions ($10^6 \times$) in gradient size, while maintaining comparable levels of accuracy, on benchmark real-world datasets.

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Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang. 2023-11-14. Sparsity-Preserving Differentially Private Training of Large Embedding Models. https://arxiv.org/abs/2311.08357

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