Synthetic Data: When Generative AI Meets Privacy in Machine Learning

The article discusses synthetic data, which is generated using machine learning algorithms and helps bypass privacy laws while still providing useful training datasets for AI models. Synthetic data can be created for any type of dataset, from simple tabular data to complex unstructured data, using various techniques such as Variational Auto-Encoders (VAE), Generative Adversarial Networks (GAN), and Diffusion Models. The use of synthetic data is particularly valuable in industries with privacy concerns or limited access to quality data, such as healthcare, finance, and AI research. Synthetic data can help build more realistic language models by providing high-quality training data and addressing biases present in real-world datasets. However, the reliance on real-world data for generating synthetic data raises concerns about maintaining privacy and ensuring accurate representation of the original data.