Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/DataResponsibly/DataSynthesizer

Last synced: about 1 month ago
JSON representation

Host: GitHub
URL: https://github.com/DataResponsibly/DataSynthesizer
Owner: DataResponsibly
License: mit
Created: 2017-01-26T19:50:06.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-04-03T09:45:42.000Z (3 months ago)
Last Synced: 2024-05-10T22:09:12.174Z (about 2 months ago)
Language: HTML
Size: 33.3 MB
Stars: 240
Watchers: 14
Forks: 83
Open Issues: 14
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE

Lists

AwesomeResponsibleAI - DataSynthesizer: Privacy-Preserving Synthetic Datasets
awesome-PETs - DataSynthesizer - A Python-based tool for generating differentally-private synthetic data (Synthetic Data / Tools)
awesome-privacy-engineering - Data Synthesizer - DataSynthesizer generates synthetic data that simulates a given dataset. (Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) / Synthetic Data)
awesome-data-synthesis - DataSynthesizer - DataSynthesizer generates synthetic data that simulates a given dataset. It applies Differential Privacy techniques to achieve strong privacy guarantee. (Data-driven methods / Tabular)
awesome-data-centric-ai - DataSynthesizer - DataSynthesizer generates synthetic data that simulates a given dataset. It applies Differential Privacy techniques to achieve a strong privacy guarantee. (🔐 Synthetic Data)

README

        [![PyPi Shield](https://img.shields.io/pypi/v/DataSynthesizer.svg)](https://pypi.python.org/pypi/DataSynthesizer) [![Travis CI Shield](https://travis-ci.com/DataResponsibly/DataSynthesizer.svg?branch=master)](https://travis-ci.com/DataResponsibly/DataSynthesizer)

# 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](docs/cr-datasynthesizer-privacy.pdf)

### Install DataSynthesizer

```bash

pip install DataSynthesizer

```

### Usage

##### Assumptions for the Input Dataset

1. The input dataset is a table in first normal form ([1NF](https://en.wikipedia.org/wiki/First_normal_form)).

2. When implementing differential privacy, DataSynthesizer injects noises into the statistics within **active domain** that are the values presented in the table.

##### Use Jupyter Notebook

After installing DataSynthesizer and [Jupyter Notebook](https://jupyter.org/install), open and try the demos in `./notebooks/`

- [DataSynthesizer__random_mode.ipynb](notebooks/DataSynthesizer__random_mode.ipynb)

- [DataSynthesizer__independent_attribute_mode.ipynb](notebooks/DataSynthesizer__independent_attribute_mode.ipynb)

- [DataSynthesizer__correlated_attribute_mode.ipynb](notebooks/DataSynthesizer__correlated_attribute_mode.ipynb)

##### Use Web UI

The [dataResponsiblyUI](https://github.com/DataResponsibly/dataResponsiblyUI) is a Django project that includes DataSynthesizer. Please follow the steps in [Run the Web UIs locally](https://github.com/DataResponsibly/dataResponsiblyUI#run-the-web-uis-locally) and run DataSynthesizer by visiting http://127.0.0.1:8000/synthesizer in a browser.