DataSynthesizer

Usage

DataSynthesizer can generate a synthetic dataset from a sensitive one for release to public. It is developed in Python 3.6 and requires some third-party modules, including numpy, scipy, pandas, and dateutil.

Its usage is presented in the following Jupyter Notebooks,

DataSynthesizer Usage (random mode).ipynb
DataSynthesizer Usage (independent attribute mode).ipynb
DataSynthesizer Usage (correlated attribute mode).ipynb

Assumptions for Input Dataset

The input dataset is a table in first normal form (1NF).
The active domain is the domain for each attribute of the table. When implementing differential privacy, DataSynthesizer injects noises into the statistics within active domain.

Web-based UI

There is a web-based UI in webUI/ directory, which is a self-contained Django project. Here is a simple way to run it on your machine.

Step 1 Install Python 3 and necessary packages

Miniconda is recommended as the Python distribution. It also contains a user-friendly package manager "conda". Note that DataSynthesizer is Python 3 based.
After installing it on your machine, run conda install numpy pandas scikit-learn matplotlib seaborn jupyter django in terminal to install the packages.

Step 2 Run web-based UI