  Data serves as the fundamental foundation for advancing deep learning,
particularly tabular data presented in a structured format, which is highly
conducive to modeling. However, even in the era of LLM, obtaining tabular data
from sensitive domains remains a challenge due to privacy or copyright
concerns. Hence, exploring how to effectively use models like LLMs to generate
realistic and privacy-preserving synthetic tabular data is urgent. In this
paper, we take a step forward to explore LLMs for tabular data synthesis and
privacy protection, by introducing a new framework HARMONIC for tabular data
generation and evaluation. In the tabular data generation of our framework,
unlike previous small-scale LLM-based methods that rely on continued
pre-training, we explore the larger-scale LLMs with fine-tuning to generate
tabular data and enhance privacy. Based on idea of the k-nearest neighbors
algorithm, an instruction fine-tuning dataset is constructed to inspire LLMs to
discover inter-row relationships. Then, with fine-tuning, LLMs are trained to
remember the format and connections of the data rather than the data itself,
which reduces the risk of privacy leakage. In the evaluation part of our
framework, we develop specific privacy risk metrics DLT for LLM synthetic data
generation, as well as performance evaluation metrics LLE for downstream LLM
tasks. Our experiments find that this tabular data generation framework
achieves equivalent performance to existing methods with better privacy, which
also demonstrates our evaluation framework for the effectiveness of synthetic
data and privacy risks in LLM scenarios.
