Exploring the benefits of differentially private pre-training and fine-tuning for table transformers

For machine learning with tabular data, a table transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy. In this paper, we explore the benefits of combining these two aspects together in the scenario of transfer learning, differentially private pretraining and fine-tuning of TabTransformers with a variety of Parameter-Efficient Fine-Tuning (PEFT) methods, including adapter, LoRA, and prompt tuning. Our extensive experiments on four ACS datasets with different configurations show that these PEFT methods outperform traditional approaches in terms of the accuracy of the downstream task and the number of trainable parameters, thus achieving an improved trade-off among parameter efficiency, privacy, and accuracy.