PrivTab embeds a differential-privacy mechanism inside a tabular foundation model, beating DP-LR and DP-MLP on 33 TabArena datasets under moderate-to-strong privacy while cutting fitting time from minutes to about 10 milliseconds
The authors introduce PrivTab, a tabular foundation model that embeds a differential-privacy mechanism in its architecture: pretrained on simulated datasets, it uses in-context learning to compress sensitive rows into compact, provably private summaries, outperforming DP-LR and DP-MLP on 33 TabArena classification datasets across six privacy regimes under moderate-to-strong privacy, maintaining well-calibrated predictions, and reducing fitting time from minutes to a single forward pass of about 10 milliseconds.