Synthetic data is machine-generated information that mimics real data without revealing private details. It is created through algorithms that analyze existing data sets to understand statistical relationships, allowing for the generation of new data points that retain these relationships while ensuring privacy.

Synthetic data helps improve privacy by allowing researchers to use data that does not contain identifiable information about individuals. This is particularly important in fields like healthcare, where sensitive data is often involved. By using synthetic data, organizations can mitigate the risk of exposing personal information while still training AI systems effectively.

Researchers encounter several challenges with synthetic data, including ensuring the accuracy of the generated data and maintaining the balance between privacy and utility. Additionally, there is a need for better understanding of how synthetic data can reveal private information and the complexities involved in creating data sets that accurately reflect the nuances of real-world data.