Synthetic Data Generator (SDG) is a framework focused on quickly generating high-quality structured tabular data. It supports many single-table and multi-table data synthesis algorithms, achieving up to 120 times performance improvement, and supports differential privacy and other methods to enhance the security of synthesized data.
Synthetic data is generated by machines based on real data and algorithms, it does not contain sensitive information, but can retain the characteristics of real data. There is no correspondence between synthetic data and real data, and it is not subject to privacy regulations such as GDPR and ADPPA. In practical applications, there is no need to worry about the risk of privacy leakage. High-quality synthetic data can also be used in various fields such as data opening, model training and debugging, system development and testing, etc.
- high performance
- Supports a wide range of statistical data synthesis algorithms to achieve up to 120x performance improvement, without the need for GPU devices;
- Optimised for big data scenarios, effectively reducing memory consumption;
- Continuously tracking the latest advances in academia and industry, and introducing support for excellent algorithms and models in a timely manner.
- Provide distributed training support for deep learning models with frameworks such as torch.
- Privacy enhancements:
- SDG supports differential privacy, anonymization and other methods to enhance the security of synthetic data.
- Easy to extend
- Supports expansion of models, data processing, data connectors, etc. in the form of plug-in packages
Read the latest API docs for more details.
You can use pre-built images to quickly experience the latest features.
docker pull idsteam/sdgx:latestAt present, the code of this project is updated very quickly. We recommend that you use SDG by installing it through the source code.
git clone git@github.com:hitsz-ids/synthetic-data-generator.git
pip install .
# Or install from git
pip install git+https://github.com/hitsz-ids/synthetic-data-generator.gitpip install sdgxfrom sdgx.data_connectors.csv_connector import CsvConnector
from sdgx.models.ml.single_table.ctgan import CTGANSynthesizerModel
from sdgx.synthesizer import Synthesizer
from sdgx.utils import download_demo_data
# This will download demo data to ./dataset
dataset_csv = download_demo_data()
# Create data connector for csv file
data_connector = CsvConnector(path=dataset_csv)
# Initialize synthesizer, use CTGAN model
synthesizer = Synthesizer(
model=CTGANSynthesizerModel(epochs=1), # For quick demo
data_connector=data_connector,
)
# Fit the model
synthesizer.fit()
# Sample
sampled_data = synthesizer.sample(1000)
print(sampled_data)Real data are as follows：
>>> data_connector.read()
age workclass fnlwgt education ... capitalloss hoursperweek native-country class
0 2 State-gov 77516 Bachelors ... 0 2 United-States <=50K
1 3 Self-emp-not-inc 83311 Bachelors ... 0 0 United-States <=50K
2 2 Private 215646 HS-grad ... 0 2 United-States <=50K
3 3 Private 234721 11th ... 0 2 United-States <=50K
4 1 Private 338409 Bachelors ... 0 2 Cuba <=50K
... ... ... ... ... ... ... ... ... ...
48837 2 Private 215419 Bachelors ... 0 2 United-States <=50K
48838 4 NaN 321403 HS-grad ... 0 2 United-States <=50K
48839 2 Private 374983 Bachelors ... 0 3 United-States <=50K
48840 2 Private 83891 Bachelors ... 0 2 United-States <=50K
48841 1 Self-emp-inc 182148 Bachelors ... 0 3 United-States >50K
[48842 rows x 15 columns]Synthetic data are as follows：
>>> sampled_data
age workclass fnlwgt education ... capitalloss hoursperweek native-country class
0 1 NaN 28219 Some-college ... 0 2 Puerto-Rico <=50K
1 2 Private 250166 HS-grad ... 0 2 United-States >50K
2 2 Private 50304 HS-grad ... 0 2 United-States <=50K
3 4 Private 89318 Bachelors ... 0 2 Puerto-Rico >50K
4 1 Private 172149 Bachelors ... 0 3 United-States <=50K
.. ... ... ... ... ... ... ... ... ...
995 2 NaN 208938 Bachelors ... 0 1 United-States <=50K
996 2 Private 166416 Bachelors ... 2 2 United-States <=50K
997 2 NaN 336022 HS-grad ... 0 1 United-States <=50K
998 3 Private 198051 Masters ... 0 2 United-States >50K
999 1 NaN 41973 HS-grad ... 0 2 United-States <=50K
[1000 rows x 15 columns]The SDG project was initiated by Institute of Data Security, Harbin Institute of Technology. If you are interested in out project, welcome to join our community. We welcome organizations, teams, and individuals who share our commitment to data protection and security through open source:
- Read CONTRIBUTING before draft a pull request.
- Submit an issue by viewing View First Good Issue or submit a Pull Request.
- CTGAN：Modeling Tabular Data using Conditional GAN
- TVAE：Modeling Tabular Data using Conditional GAN
- table-GAN：Data Synthesis based on Generative Adversarial Networks
- CTAB-GAN:CTAB-GAN: Effective Table Data Synthesizing
- OCT-GAN: OCT-GAN: Neural ODE-based Conditional Tabular GANs
The SDG open source project uses Apache-2.0 license, please refer to the LICENSE.