What is Privacy-Preserving Techniques

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.