Noise-Aware Differentially Private Regression via Meta-Learning

Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the gold standard for protecting user privacy, standard DP mechanisms typically significantly impair performance. One approach to mitigating this issue is pre-training models on simulated data before DP learning on the private data. In this work we go a step further, using simulated data to train a meta-learning model that combines the Convolutional Conditional Neural Process (ConvCNP) with an improved functional DP mechanism of Hall et al. [2013] yielding the DPConvCNP. DPConvCNP learns from simulated data how to map private data to a DP predictive model in one forward pass, and then provides accurate, well-calibrated predictions. We compare DPConvCNP with a DP Gaussian Process (GP) baseline with carefully tuned hyperparameters. The DPConvCNP outperforms the GP baseline, especially on non-Gaussian data, yet is much faster at test time and requires less tuning.

@article{räisä2025_2406.08569,
  title={ Noise-Aware Differentially Private Regression via Meta-Learning },
  author={ Ossi Räisä and Stratis Markou and Matthew Ashman and Wessel P. Bruinsma and Marlon Tobaben and Antti Honkela and Richard E. Turner },
  journal={arXiv preprint arXiv:2406.08569},
  year={ 2025 }
}