A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond

• URL: http://arxiv.org/abs/2503.05954v1
• Date: Fri, 07 Mar 2025 21:47:11 GMT
• Title: A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond
• Authors: Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz,
• Abstract summary: Different use cases demand synthetic data to comply with different requirements to be useful in practice.<n>Four types of requirements are reviewed: utility of the synthetic data, alignment of the synthetic data with domain-specific knowledge, statistical fidelity of the synthetic data distribution compared to the real data distribution, and privacy-preserving capabilities.<n>We discuss future directions for the field, along with opportunities to improve the current evaluation methods.
• Score: 53.56796220109518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative modelling has become the standard approach for synthesising tabular data. However, different use cases demand synthetic data to comply with different requirements to be useful in practice. In this survey, we review deep generative modelling approaches for tabular data from the perspective of four types of requirements: utility of the synthetic data, alignment of the synthetic data with domain-specific knowledge, statistical fidelity of the synthetic data distribution compared to the real data distribution, and privacy-preserving capabilities. We group the approaches along two levels of granularity: (i) based on the primary type of requirements they address and (ii) according to the underlying model they utilise. Additionally, we summarise the appropriate evaluation methods for each requirement and the specific characteristics of each model type. Finally, we discuss future directions for the field, along with opportunities to improve the current evaluation methods. Overall, this survey can be seen as a user guide to tabular data generation: helping readers navigate available models and evaluation methods to find those best suited to their needs.

Related papers

• Opinion: Revisiting synthetic data classifications from a privacy perspective [42.12937192948916]
  Synthetic data is emerging as a cost-effective solution to meet the increasing data demands of AI development. Traditional classification of synthetic data types does not reflect the ever-increasing methods to generate synthetic data. We make a case for an alternative approach to grouping synthetic data types that better reflect privacy perspectives.
  arXiv  Detail & Related papers  (2025-03-05T13:54:13Z)
• 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)
• A Comprehensive Survey on Data Augmentation [55.355273602421384]
  Data augmentation is a technique that generates high-quality artificial data by manipulating existing data samples. Existing literature surveys only focus on a certain type of specific modality data. We propose a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities.
  arXiv  Detail & Related papers  (2024-05-15T11:58:08Z)
• Preserving correlations: A statistical method for generating synthetic data [0.0]
  We propose a method to generate statistically representative synthetic data from a given dataset. The main goal of our method is for the created data to mimic the between feature correlations present in the original data. We describe in detail our algorithms used both in the construction of a statistical map and how to use this map to generate synthetic observations.
  arXiv  Detail & Related papers  (2024-03-03T10:35:46Z)
• 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)
• Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results [0.32593385688760446]
  In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data. In this study, we use a state-of-the-art synthetic data generation method.
  arXiv  Detail & Related papers  (2023-05-12T13:13:55Z)
• Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases [12.358921226358133]
  The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing need to have access to synthetic data to test certain concepts and ideas.
  arXiv  Detail & Related papers  (2022-10-16T13:21:23Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Foundations of Bayesian Learning from Synthetic Data [1.6249267147413522]
  We use a Bayesian paradigm to characterise the updating of model parameters when learning on synthetic data. Recent results from general Bayesian updating support a novel and robust approach to synthetic-learning founded on decision theory.
  arXiv  Detail & Related papers  (2020-11-16T21:49:17Z)
• Partially Conditioned Generative Adversarial Networks [75.08725392017698]
  Generative Adversarial Networks (GANs) let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy.
  arXiv  Detail & Related papers  (2020-07-06T15:59:28Z)

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.