Synthetic Data

Md Zia Uddin

Abstract: This chapter takes a closer look at what synthetic data is, why it matters, and how it fits into data science and artificial intelligence. Since collecting real-world data often comes with privacy risks and biases, synthetic data provides a safe alternative by mimicking the patterns of real data without exposing sensitive details. One of the key focus areas of this chapter is how synthetic data is making a difference in assisted living. In healthcare, for example, researchers can use synthetic patient data to train AI models for detecting diseases, monitoring patients, and predicting health outcomes—without compromising anyone’s private information. Beyond healthcare, synthetic data is also driving progress in the automotive industry, security systems, and natural language processing. We also explore different ways to create synthetic data, from traditional statistical methods to modern machine learning techniques like Generative Adversarial Networks (GANs). Various types of GAN models, including CTGAN, CycleGAN, MedGAN, and WGAN, are discussed to show how they generate realistic and diverse datasets. This is especially useful in assisted living, where high-quality synthetic data can improve AI-powered tools like fall detection systems, behavioral analysis, and personalized care solutions. Another important topic is fairness in synthetic data. AI models trained on biased data can lead to unfair outcomes, which is a serious issue in healthcare and assisted living. This chapter explains how to create fair synthetic data and reduce bias so that AI models work equally well for everyone. Techniques such as balancing class distributions and adversarial debiasing are introduced to ensure synthetic datasets are both accurate and ethical. To help readers put theory into practice, we include step-by-step guidance on generating synthetic data using Python. Examples cover applications in regression, classification, and clustering, as well as generating tabular synthetic data using GANs. By applying these methods, professionals working in healthcare, security, and AI development can overcome data limitations and improve their models in meaningful ways. Synthetic data is proving to be a powerful tool for AI, especially in fields where privacy, fairness, and data quality are essential. This chapter aims to give readers a well-rounded understanding of what synthetic data is, how it works, and how it can be used to solve real-world challenges.

External IDs:doi:10.1007/978-3-031-97359-8_3