LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification

arXiv:2402.16515v1 Announce Type: cross
Abstract: As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA). Conducting DA in private domain requires private protection approaches (i.e. anonymization and perturbation), but those methods cannot provide protection guarantees. Differential privacy (DP) learning methods theoretically bound the protection but are not skilled at generating pseudo text samples with large models. In this paper, we transfer DP-based pseudo …

advanced algorithms anonymization arxiv augmentation classification cs.cl cs.cr data dataset distribution domain exploit knowledge llm medical model training privacy private protection researchers text training