Synthetic Data Generation

Synthetic Data Generation is the practice of creating artificial datasets that mimic the statistical properties and edge-case diversity of real-world data without exposing sensitive information. Techniques range from rule-based simulators and generative adversarial networks (GANs) to diffusion models and large language models (LLMs) that output tabular rows, images, or text. A workflow ingests seed data or domain schemas, trains a generator, and validates output with privacy checks (k-anonymity, differential privacy) and fidelity metrics such as Jensen-Shannon divergence. Synthetic data augments scarce classes, balances imbalanced labels, and fuels machine-learning training when legal or ethical constraints block raw data sharing. Used in healthcare, finance, and autonomous driving, it accelerates model development, reduces annotation cost, and supports robust testing for edge conditions. Challenges include mode collapse, hidden bias replication, and regulatory acceptance, mitigated by rigorous evaluation and hybrid mixes with real samples.