DataSynthesizer 0.1.3

Generate synthetic data that simulate a given dataset.

DataSynthesizer

DataSynthesizer generates synthetic data that simulates a given dataset.

It aims to facilitate the collaborations between data scientists and owners of sensitive data. It applies Differential Privacy techniques to achieve strong privacy guarantee.

For more details, please refer to DataSynthesizer: Privacy-Preserving Synthetic Datasets

Install DataSynthesizer

pip install DataSynthesizer

Usage

Assumptions for the Input Dataset

1. The input dataset is a table in first normal form (1NF).
2. When implementing differential privacy, DataSynthesizer injects noises into the statistics within active domain that are the values presented in the table.

Use Jupyter Notebooks

# install jupyter first
pip install jupyter

There are some demos in ./notebooks/

• DataSynthesizer__random_mode.ipynb
• DataSynthesizer__independent_attribute_mode.ipynb
• DataSyntheiszer__correlated_attribute_mode.ipynb

Use webUI

DataSynthesizer can be executed by a web-based UI.

# install django
pip install django

# go to the directory for webUI
cd DataSynthesizer/webUI/

# run the server
python manage.py runserver

Then open a browser and visit http://127.0.0.1:8000/synthesizer/

History

0.1.0 - 2020-06-11

• First release on PyPI.

0.1.1 - 2020-07-05

Bugs Fixed

• Numpy error when synthesising data with unique identifiers. - Issue #23 by @raids

0.1.2 - 2020-07-19

Bugs Fixed

• infer_distribution() for string attributes fails to sort index of varying types. - Issue #24 by @raids

0.1.3 - 2020-09-13

Bugs Fixed

• The dataframes are not appended into the full space in get_noisy_distribution_of_attributes(). - Issue #26 by @zjroth