hitsz-ids/synthetic-data-generator

Features

• Tabular - Creates privacy-preserving synthetic tabular records that maintain the statistical properties of original source data.
• Data Synthesis - Leverages language models and metadata to synthesize information when original training samples are restricted.
• Synthetic Data Generators - Creates privacy-preserving structured datasets that maintain the statistical properties of original source data.
• Synthetic Data Generators - Generates synthetic datasets using metadata and language model knowledge without requiring original training samples.
• Relational Synthesis - Generates consistent synthetic datasets across multiple related tables while preserving structural integrity and logical relationships.
• Statistical Fidelity Evaluators - Validates synthetic data quality by comparing distribution and correlation patterns against original source datasets.
• Privacy and Data Protection - Protects sensitive information using differential privacy and masking techniques to ensure compliance.
• Differential Privacy Aggregators - Provides a toolkit for quantifying and mitigating re-identification risks in synthetic datasets.
• Differential Privacy Noise Injection - Injects mathematical noise into synthetic data generation to ensure individual records cannot be re-identified.
• Schema-Driven Generators - Constructs synthetic datasets by interpreting structured schema definitions rather than relying on raw input samples.
• Relational Data Generators - Generates consistent synthetic data across multiple related tables while preserving structural integrity.
• Synthetic Data Generation - Enriches datasets by generating new columns based on learned patterns to improve data depth.
• Data Enrichment - Enriches datasets by deriving new columns from learned patterns and external knowledge.
• Security & Privacy - Identifies personal identifiers and applies privacy techniques to secure generated information.
• Memory-Efficient Data Streaming - Processes large-scale datasets in memory-efficient chunks to maintain system stability during high-volume generation.
• Graph-Relational Models - Maintains logical consistency across multi-table structures by enforcing structural dependencies during data generation.
• Relational Data Modeling - Models complex multi-table relationships to ensure logical consistency during synthetic data generation.
• Generation Framework Extensions - Allows integration of custom generation models, data processing routines, and external connectors through a modular plugin architecture.

Star history