# hitsz-ids/synthetic-data-generator **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/hitsz-ids-synthetic-data-generator).** 2,422 stars · 391 forks · Python · Apache-2.0 ## Links - GitHub: https://github.com/hitsz-ids/synthetic-data-generator - awesome-repositories: https://awesome-repositories.com/repository/hitsz-ids-synthetic-data-generator.md ## Topics `agent` `data-generator` `deep-learning` `gan` `generative-ai` `llm` `machine-learning` `privacy` `synthetic-data` `tabular-data` ## Description This project is a framework for generating synthetic tabular data that preserves the statistical properties and relational integrity of original source datasets. It functions as a metadata-driven engine, utilizing language models to synthesize information even when original training samples are restricted. The system is designed to maintain logical consistency across complex, multi-table structures while ensuring that generated outputs adhere to defined schema requirements. The platform distinguishes itself through a focus on privacy-preserving synthesis, integrating tools to quantify and mitigate re-identification risks through differential privacy and anonymization techniques. It supports modular extensibility, allowing for the integration of custom generation models and data connectors. Furthermore, the framework includes automated validation routines that compare the distribution and correlation patterns of synthetic outputs against source data to verify statistical fidelity. Beyond core generation, the system provides capabilities for data enrichment and feature engineering by deriving new columns from learned patterns. It incorporates operational oversight tools to monitor resource utilization and processing efficiency during high-volume tasks. The library is designed to handle large-scale datasets through memory-efficient stream processing and iterative batching to ensure stability. ## Tags ### Artificial Intelligence & ML - [Tabular](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular.md) — Creates privacy-preserving synthetic tabular records that maintain the statistical properties of original source data. ([source](https://github.com/hitsz-ids/synthetic-data-generator#readme)) - [Data Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular/data-synthesis.md) — Leverages language models and metadata to synthesize information when original training samples are restricted. - [Synthetic Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-generation/synthetic-data-generators.md) — Creates privacy-preserving structured datasets that maintain the statistical properties of original source data. - [Synthetic Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators.md) — Generates synthetic datasets using metadata and language model knowledge without requiring original training samples. ([source](https://github.com/hitsz-ids/synthetic-data-generator#readme)) - [Relational Synthesis](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators/tabular/data-synthesis/relational-synthesis.md) — Generates consistent synthetic datasets across multiple related tables while preserving structural integrity and logical relationships. ([source](https://github.com/hitsz-ids/synthetic-data-generator/blob/main/ROADMAP_ZH_CN.md)) - [Statistical Fidelity Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/text-to-sql-translators/accuracy-evaluators/statistical-fidelity-evaluators.md) — Validates synthetic data quality by comparing distribution and correlation patterns against original source datasets. - [Synthetic Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generation.md) — Enriches datasets by generating new columns based on learned patterns to improve data depth. ([source](https://github.com/hitsz-ids/synthetic-data-generator/blob/main/README_ZH_CN.md)) ### Security & Cryptography - [Privacy and Data Protection](https://awesome-repositories.com/f/security-cryptography/privacy-data-protection.md) — Protects sensitive information using differential privacy and masking techniques to ensure compliance. - [Differential Privacy Aggregators](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-utilities/differential-privacy-aggregators.md) — Provides a toolkit for quantifying and mitigating re-identification risks in synthetic datasets. - [Differential Privacy Noise Injection](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-utilities/differential-privacy-aggregators/differential-privacy-noise-injection.md) — Injects mathematical noise into synthetic data generation to ensure individual records cannot be re-identified. ### Software Engineering & Architecture - [Schema-Driven Generators](https://awesome-repositories.com/f/software-engineering-architecture/schema-driven-generators.md) — Constructs synthetic datasets by interpreting structured schema definitions rather than relying on raw input samples. ### Testing & Quality Assurance - [Relational Data Generators](https://awesome-repositories.com/f/testing-quality-assurance/relational-data-generators.md) — Generates consistent synthetic data across multiple related tables while preserving structural integrity. ### Part of an Awesome List - [Data Enrichment](https://awesome-repositories.com/f/awesome-lists/data/data-enrichment.md) — Enriches datasets by deriving new columns from learned patterns and external knowledge. - [Security & Privacy](https://awesome-repositories.com/f/awesome-lists/security/security-privacy.md) — Identifies personal identifiers and applies privacy techniques to secure generated information. ([source](https://github.com/hitsz-ids/synthetic-data-generator#readme)) ### Data & Databases - [Memory-Efficient Data Streaming](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/stream-processing-systems/stream-processing/memory-efficient-data-streaming.md) — Processes large-scale datasets in memory-efficient chunks to maintain system stability during high-volume generation. - [Graph-Relational Models](https://awesome-repositories.com/f/data-databases/graph-data-models/graph-relational-models.md) — Maintains logical consistency across multi-table structures by enforcing structural dependencies during data generation. - [Relational Data Modeling](https://awesome-repositories.com/f/data-databases/relational-data-modeling.md) — Models complex multi-table relationships to ensure logical consistency during synthetic data generation. ### Programming Languages & Runtimes - [Generation Framework Extensions](https://awesome-repositories.com/f/programming-languages-runtimes/class-inheritance/native-class-subclassing/plugin-and-subclass-extension-systems/generation-framework-extensions.md) — Allows integration of custom generation models, data processing routines, and external connectors through a modular plugin architecture. ([source](https://github.com/hitsz-ids/synthetic-data-generator/blob/main/README_ZH_CN.md))