Noise-Aware Differentially Private Regression via Meta-Learning

TL;DR

This paper introduces DPConvCNP, a meta-learning approach that uses simulated data to train a differentially private model capable of providing accurate and well-calibrated predictions efficiently, outperforming traditional Gaussian Process baselines.

Contribution

The paper presents a novel meta-learning method combining ConvCNP with an improved functional DP mechanism, enabling fast, accurate private predictions from simulated data.

Findings

DPConvCNP outperforms DP Gaussian Process baseline on non-Gaussian data

DPConvCNP is faster at test time and requires less hyperparameter tuning

Meta-learning with simulated data enhances privacy-preserving predictive modeling

Abstract

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.