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For machine learning with tabular data, Table Transformer (TabTransformer) isa state-of-the-art neural network model, while Differential Privacy (DP) is anessential component to ensure data privacy. In this paper, we explore thebenefits of combining these two aspects together in the scenario of transferlearning -- differentially private pre-training and fine-tuning ofTabTransformers with a variety of parameter-efficient fine-tuning (PEFT)methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experimentson the ACSIncome dataset show that these PEFT methods outperform traditionalapproaches in terms of the accuracy of the downstream task and the number oftrainable parameters, thus achieving an improved trade-off among parameterefficiency, privacy, and accuracy. Our code is available atgithub.com/IBM/DP-TabTransformer.

Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers