Pysyneet

Pysyneet is a Python-based software framework for synthetic data generation and evaluation in machine learning and data science. The project provides an end-to-end workflow for creating realistic yet privacy-preserving datasets and for assessing their usefulness relative to real data.

Core features include a modular pipeline with data models, generator implementations, privacy controls, and evaluators for statistical similarity and downstream model utility. The framework is designed to integrate with common Python data tools such as NumPy, pandas, scikit-learn, PyTorch, and TensorFlow.

Architecturally, pysyneet uses producers, transformers, and evaluators arranged in pipelines. Users can define synthesis recipes, configure privacy budgets, and run reproducible experiments. Configurations may be authored in JSON or YAML, and the framework emphasizes traceability through versioned workflows and audit trails.

Origin and license: pysyneet emerged in the early 2020s from a community of researchers and practitioners and is released as open-source under a permissive license. It has attracted contributions from universities and industry partners and maintains an active community around documentation and examples.

Use and reception: In academia, pysyneet is used to study privacy-preserving data synthesis and to benchmark model performance on synthetic data. In industry, it supports data governance and rapid prototyping. Critics warn that synthetic data can still leak sensitive information if privacy controls are insufficient or misapplied.

See also: synthetic data, differential privacy, data governance, Python data science libraries.