Noise-Aware Differentially Private Regression via Meta-Learning

arXiv:2406.08569v1 Announce Type: cross
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 …

applications arxiv aware cs.cr cs.lg data differential privacy high issue machine machine learning machine learning models mechanisms meta noise performance predictions privacy private protect protecting standard stat.ml training user privacy