Abstract
This chapter offers a comprehensive examination of contemporary practices in synthetic data generation. Its primary objective is to analyze and synthesize the methodologies, techniques, applications, and challenges associated with synthetic data across diverse scientific disciplines. The motivation behind the use of synthetic data stems from data privacy concerns, limitations in data availability, and the necessity for diverse, representative datasets. This chapter delves into various synthetic data generation methods, such as statistical modeling, generative adversarial networks (GANs), simulation-based techniques, and data envelopment analysis (DEA). It also scrutinizes the evaluation metrics for assessing synthetic data quality and privacy preservation. The chapter highlights applications in healthcare, finance, social sciences, and computer vision, and discusses emerging trends, including deep learning integration and domain adaptation. Researchers, practitioners, and policymakers will gain valuable insights into the state-of-the-art in synthetic data generation.
TopIntroduction
Synthetic data is a type of artificially generated data designed to replicate the statistical properties of real-world data. It is created using algorithms and simulation models. Synthetic data serves a crucial role in situations where access to real data is limited, restricted due to privacy concerns, or when data is scarce. It has become particularly valuable in training machine learning models, testing systems, and conducting research while ensuring data privacy and compliance with data protection regulations. The concept of synthetic data has evolved significantly over time, progressing from simple statistical methods to advanced AI-driven models. Initially, it was developed to address data scarcity and privacy issues by using statistical sampling and resampling techniques to replicate statistical properties without revealing sensitive information (Rubin, 1993). Over time, complex simulation models, like agent-based modeling, broadened the scope of synthetic data by capturing dynamic interactions and behaviors within datasets (Bonabeau, 2002). These developments had a profound impact on fields such as social sciences and epidemiology. The introduction of Generative Adversarial Networks (GANs) by Goodfellow et al. (2014) represented a major leap forward in synthetic data generation, along with techniques like Variational Autoencoders (Kingma & Welling, 2014), which contributed to creating more realistic and diverse synthetic datasets.
Synthetic data generation has garnered substantial attention as a valuable technique in data-driven research and applications (Assefa et al., 2020; Tyxhari & Martiri, 2022). It offers solutions to challenges associated with limited access to real-world datasets, which may result from privacy concerns, data scarcity, or the need for representative and diverse datasets (Bonnéry et al., 2019). This chapter provides a comprehensive examination of the methodologies, techniques, applications, and challenges related to synthetic data generation across various scientific disciplines.
One primary motivation for using synthetic data is the growing concern for data privacy. Real-world datasets often contain sensitive information about individuals or organizations, making it difficult to share for research purposes (Drechsler et al., 2019). Synthetic data provides a means to generate privacy-preserving substitutes that maintain essential statistical properties while safeguarding individual privacy. Another motivation is addressing data scarcity. In many research domains, obtaining a large and diverse dataset can be time-consuming, expensive, or impractical (Monroe et al., 2018; Bansal et al., 2022). Synthetic data generation techniques enable researchers to create additional data instances, thereby augmenting the available dataset and enabling more comprehensive analyses and model training. Furthermore, synthetic data generation helps ensure the availability of representative and diverse datasets. Real-world datasets can suffer from biases or limited coverage of specific data patterns, potentially impacting the performance and generalization of models trained on such data (Le et al., 2017). Synthetic data can mitigate these issues by creating instances that encompass a broader range of data patterns and ensure a more representative distribution.
Key Terms in this Chapter
Synthetic Data: Artificially generated data that mimics the statistical properties of real-world data, typically used to protect privacy, overcome data limitations, or improve machine learning models.
Explainability: The degree to which synthetic data generation processes and models can be understood and justified, fostering trust and transparency in their use.
Representativeness: Indicates how well the synthetic data captures the complexities and nuances of the real-world data it aims to replicate, without introducing biases.
Privacy-Preserving Techniques: Methods and strategies used to protect sensitive information in datasets, allowing data sharing and analysis without revealing personal or confidential details.
Synthetic Data Bias: Systematic errors or inaccuracies in data, often resulting from biased sampling or other data generation methods, that can lead to unfair or misleading conclusions.
Generalization: The ability of models or algorithms trained on one dataset (synthetic data) to perform well on unseen data, reflecting the model's capacity to apply knowledge learned from the training data to new, real-world scenarios.
Evaluation Metrics and Benchmarks: Quantifiable standards used to assess the performance and quality of synthetic data. Benchmarks are standardized datasets that enable comparisons between different synthetic data generation methods.