SDV: a machine learning library for generating and evaluating privacy-preserving tabular synthetic data

What it solves

SDV provides a comprehensive toolkit for creating high-quality tabular synthetic data. It solves the problem of needing realistic data for testing or analysis without exposing sensitive real-world information, allowing users to share or use data while maintaining privacy through anonymization.

How it works

The library uses various machine learning algorithms to learn the statistical patterns, correlations, and relationships within a real dataset. It then emulates these patterns to generate new, synthetic rows of data. It supports multiple modeling approaches, ranging from classical statistical methods like Gaussian Copulas to deep learning models like CTGAN.

Who it’s for

Data scientists and developers who need to generate synthetic versions of single tables, multiple connected tables, or sequential data for software testing, research, or privacy-preserving data sharing.

Highlights

• Diverse Modeling Options: Supports both statistical and deep learning models for data synthesis.
• Privacy-Focused: Includes tools for anonymizing sensitive columns and defining business rules as logical constraints.
• Comprehensive Evaluation: Provides built-in tools to compare synthetic data against real data using quality reports and visualizations.
• Flexible Data Structures: Capable of synthesizing single tables, multi-table relational databases, and sequential/time-series data.