Differentially Private Tabular Data Synthesis using Large Language Models

• URL: http://arxiv.org/abs/2406.01457v1
• Date: Mon, 3 Jun 2024 15:43:57 GMT
• Title: Differentially Private Tabular Data Synthesis using Large Language Models
• Authors: Toan V. Tran, Li Xiong,
• Abstract summary: This paper introduces DP-LLMTGen -- a novel framework for differentially private tabular data synthesis. DP-LLMTGen models sensitive datasets using a two-stage fine-tuning procedure. It generates synthetic data through sampling the fine-tuned LLMs.
• Score: 6.6376578496141585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic tabular data generation with differential privacy is a crucial problem to enable data sharing with formal privacy. Despite a rich history of methodological research and development, developing differentially private tabular data generators that can provide realistic synthetic datasets remains challenging. This paper introduces DP-LLMTGen -- a novel framework for differentially private tabular data synthesis that leverages pretrained large language models (LLMs). DP-LLMTGen models sensitive datasets using a two-stage fine-tuning procedure with a novel loss function specifically designed for tabular data. Subsequently, it generates synthetic data through sampling the fine-tuned LLMs. Our empirical evaluation demonstrates that DP-LLMTGen outperforms a variety of existing mechanisms across multiple datasets and privacy settings. Additionally, we conduct an ablation study and several experimental analyses to deepen our understanding of LLMs in addressing this important problem. Finally, we highlight the controllable generation ability of DP-LLMTGen through a fairness-constrained generation setting.

Related papers

• Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis [0.0]
  This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize Malicious Network Traffic. Our approach transforms numerical data into text, re-framing data generation as a language modeling task. Our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data.
  arXiv  Detail & Related papers  (2024-11-04T09:51:10Z)
• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data [10.217822818544475]
  We propose a framework to generate synthetic (tabular) data powered by large language models (LLMs) Our approach significantly enhance the quality of synthetic data generation in common scenarios with small sample sizes. Our results demonstrate that our model outperforms several state-of-art models regarding generating higher quality synthetic data for downstream tasks while keeping privacy of the real data.
  arXiv  Detail & Related papers  (2024-06-15T06:26:17Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Generating tabular datasets under differential privacy [0.0]
  We introduce Differential Privacy (DP) into the training process of deep neural networks. This creates a trade-off between the quality and privacy of the resulting data. We implement novel end-to-end models that leverage attention mechanisms.
  arXiv  Detail & Related papers  (2023-08-28T16:35:43Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Language Models are Realistic Tabular Data Generators [15.851912974874116]
  We propose GReaT (Generation of Realistic Tabular data), which exploits an auto-regressive generative large language model (LLMs) to sample synthetic and yet highly realistic data. We demonstrate the effectiveness of the proposed approach in a series of experiments that quantify the validity and quality of the produced data samples from multiple angles.
  arXiv  Detail & Related papers  (2022-10-12T15:03:28Z)
• Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification [101.1886788396803]
  Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models. In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
  arXiv  Detail & Related papers  (2021-09-12T15:51:41Z)
• Differentially Private Synthetic Medical Data Generation using Convolutional GANs [7.2372051099165065]
  We develop a differentially private framework for synthetic data generation using R'enyi differential privacy. Our approach builds on convolutional autoencoders and convolutional generative adversarial networks to preserve some of the critical characteristics of the generated synthetic data. We demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget.
  arXiv  Detail & Related papers  (2020-12-22T01:03:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.