# Introduction

PyDP is a Python wrapper for Gooogle's [Differencial Privacy](https://github.com/google/differential-privacy) project. The library provides a set of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.

PyDP is part of the OpenMined community, come join the the movement on [Slack](http://slack.openmined.org/). If you'd like to contribute to this project, and read these [Guidelines].







# Introduction

PyDP is a Python wrapper for Gooogle's [Differencial Privacy](https://github.com/google/differential-privacy) project. The library provides a set of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.

PyDP is part of the OpenMined community, come join the the movement on [Slack](http://slack.openmined.org/). If you'd like to contribute to this project, and read these [guidelines](https://github.com/OpenMined/PyDP/blob/master/contributing.md).
Update link to contributes doco
Update link to contributes doco
