Exploring Innovative Approaches to Synthetic Tabular Data Generation

The study addresses the growing demand for large, high-quality datasets in machine learning and neural networks, especially in privacy-sensitive domains like healthcare and cybersecurity. It reviews how synthetic data can alleviate data scarcity, class imbalance, and privacy risks while preserving data utility. The purpose is to provide a comprehensive overview and classification of tabular synthetic data generation methods, discuss evaluation metrics for fidelity, utility, and privacy, and highlight application areas. The paper also seeks to bridge gaps in fragmented literature by analyzing recent advances, with an emphasis on GAN-based approaches and privacy-preserving techniques. To contextualize the literature, the authors apply BERT-based topic modeling to identify key themes in recent research, informing a structured synthesis of methods and applications.

The review surveys synthetic data generation across multiple axes: (1) Data generation approaches: Statistical-based methods such as GenerativeMTD and a divide-and-conquer strategy for generating a structured tabular data dictionary that preserves logical relations, and machine learning-based methods including GAN variants (cGAN, CTGAN, TGAN), VAEs/CVAEs, and interpretable GANBLR for tabular data. (2) Data characteristics: Methods tailored to numerical, categorical, and temporal data, including multivariate time-series synthesis (e.g., MTS-TGAN). (3) Privacy preservation: GAN-based cybersecurity data synthesis for botnets and zero-day attacks, differentially private synthetic microdata for public release, and frameworks like Duo-GAN for privacy-sensitive, highly imbalanced domains. (4) Evaluation metrics: Standardized pipelines in health data assessing resemblance, utility, and privacy; TabSynDex as a universal tabular evaluation metric; utility-oriented comparisons showing CART outperforming several GAN-based techniques under certain metrics; assessments for anonymous network traffic synthesis. (5) Applications: Healthcare (diagnosis, prognosis, handling imbalance), energy and mobility forecasting, manufacturing analytics, agriculture classification, and retail-like use cases addressing data scarcity.

The authors conducted topic modeling on collected studies using BERT-based embeddings and the BERTopic library. Preprocessing included tokenization, lowercasing, stop-word removal, and lemmatization. BERT embeddings were generated for each document to capture semantic nuances. BERTopic was used to extract topics without predefining the number of topics, leveraging class-based TF-IDF (c-TF-IDF) to characterize topic terms. HDBSCAN clustering grouped documents into coherent topic clusters; representative keywords for each cluster were assigned based on prominent terms. This pipeline preserved significant words in topic descriptions and maintained semantic coherence. The approach yielded several salient topics (e.g., detection, data/privacy, synthetic/health, imbalanced/adversarial, stochastic/streamflow), visualized as bar charts with c-TF-IDF scores, demonstrating the flexibility and interpretability of deep learning-based topic modeling for large collections of scientific articles.

- Synthetic tabular data generation addresses data scarcity, imbalance, and privacy constraints across domains, improving downstream model performance. - Statistical approaches: GenerativeMTD outperforms conventional methods on small datasets while maintaining fidelity and privacy; a divide-and-conquer strategy for tabular data preserves complex logical relations and improves balance versus conditional sampling on multiple healthcare datasets. - Machine learning approaches: cGAN/CTGAN effectively augment scarce classes (e.g., improved minor crop classification); VAEs/CVAEs excel for small, imbalanced industrial data and software effort estimation; GANBLR improves performance and interpretability via explicit feature interactions and feature importance tracing. - Application highlights with quantitative indicators: MTP for sacral hiatus classification achieved about 90–93% accuracy using GAN-generated features with CNNs; GAN-based AED rhythm detection exceeded American Heart Association thresholds on imbalanced ECG data; botnet data GANs achieved roughly 80% similarity to real data after 1000 epochs; zero-day GAN synthesis reached equilibrium near 5000 iterations and enhanced validation accuracy of detectors; Duo-GAN showed about a 5% F1 difference between models trained on real versus synthetic-then-tested on real data; multivariate time-series synthesis (MTS-TGAN) closely captured real distributions and reduced predictive and discriminative errors; GAN-based energy consumption augmentation reduced prediction errors and converged faster than prior GANs; TGAN-generated pipeline data mirrored real distributions and improved model training. - Privacy-preserving methods enable public release of tabular microdata with global probabilistic differential privacy and improved accuracy over Laplace mechanisms; GAN-based augmentation strengthens intrusion detection with low-sample regimes. - Evaluation: A standardized health data pipeline assesses resemblance, utility, and privacy; TabSynDex provides a universal tabular metric outperforming existing measures for robustness. Utility comparisons indicate that, in some settings, CART-based synthesis can outperform GANs under propensity and cluster-log metrics, challenging assumptions about GAN dominance. - Topic modeling identified coherent themes (e.g., detection/privacy/synthetic/imbalanced) reflecting the field’s focus on utility, fidelity, and privacy in tabular synthesis.

Findings support the central thesis that synthetic tabular data can mitigate data scarcity, class imbalance, and privacy risks while sustaining or improving predictive performance. Evidence across healthcare, cybersecurity, energy, and manufacturing shows consistent gains from GANs, VAEs, and hybrid strategies. Interpretable models like GANBLR address the need for transparency in regulated domains. Privacy-preserving frameworks demonstrate that realistic, shareable data products can be produced with strong privacy guarantees, expanding data access without exposing raw data. The literature emphasizes that model choice and domain context matter: GAN architecture significantly affects quality; in some utility evaluations, tree-based methods rival or exceed neural approaches. Evaluation pipelines (resemblance, utility, privacy) and universal metrics like TabSynDex are crucial for fair comparisons, but existing metrics may not fully capture complex relationships, mixed data types, or domain-specific needs. Transferability across domains is promising but not guaranteed; dataset characteristics, high dimensionality, and nonlinearity can challenge purely statistical methods, while deep models may require substantial compute and careful tuning. Ethical considerations—bias, fairness, transparency, and accountability—remain essential, especially when synthetic data influence high-stakes decisions.

The review synthesizes recent advances in synthetic tabular data generation, spanning statistical and machine learning methods, privacy-preserving frameworks, evaluation pipelines, and diverse applications. It highlights that synthetic data can enhance model performance, enable data sharing under privacy constraints, and improve robustness in imbalanced or scarce-data settings. The paper underscores the importance of standardized evaluation across resemblance, utility, and privacy, and points to promising directions: more expressive and interpretable generative models; hybrid approaches that embed domain knowledge; rigorous benchmarking and reproducible evaluation; and principled frameworks for fairness, transparency, and accountability. Continued research on privacy risk measurement, domain transferability, and resource-efficient training will further mature the field and broaden real-world adoption.

- Generalizability: Effectiveness of methods varies by domain, data modality (numerical, categorical, temporal), and dataset characteristics; transferability is not guaranteed. - Evaluation constraints: Common metrics may not capture complex dependencies, mixed data types, or real-world heterogeneity; metric choice can bias conclusions. - Resource demands and expertise: Deep generative models often require significant computational resources and tuning, limiting accessibility and scalability. - Interpretability: Many high-performing models are opaque; although interpretable variants exist (e.g., GANBLR), broader explainability remains challenging. - Data availability and privacy: High-quality, representative training data are still required; privacy risks in federated or synthetic settings need better measurement and mitigation. - Review scope: As a literature-based synthesis augmented by topic modeling, conclusions depend on included studies and may reflect publication bias; standardized benchmarks across domains remain limited.