Show HN: Synth Data Studio Open-source synthetic data with differential privacy (github.com)

A new open-source tool, Synth Data Studio, has been launched to generate synthetic data with a focus on privacy, specifically tailored for healthcare and fintech sectors. This project enables users to create high-quality synthetic datasets while ensuring compliance with strict regulations such as HIPAA and GDPR. Synth Data Studio features several generation methods, including schema-based generation, which allows users to define data structures without needing existing datasets, and dataset-based ML generation, where real data can train models for synthetic output. It also incorporates advanced privacy mechanisms like differential privacy to protect sensitive information during data generation. The significance of Synth Data Studio lies in its ability to balance data utility and privacy, making it invaluable for organizations that require compliance while leveraging data for machine learning. With tools that automatically detect personally identifiable information (PII) and offer compliance reporting, it facilitates safer data sharing across departments and industries. The architecture utilizes popular frameworks like FastAPI and Next.js, along with machine learning techniques such as Conditional GANs (CTGAN) and Variational Autoencoders (TVAE), catering to diverse needs in data generation and privacy assurance. As AI/ML continues to expand, this project aligns with the growing demand for ethical data practices in regulated environments.