Metadata-Version: 2.1
Name: DiffPriv
Version: 0.0.2
Summary: A Differential Privacy Package
Home-page: https://github.com/Quantalabs/DiffPriv
Author: Quantalabs
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >= 3.8.1
Description-Content-Type: text/markdown

![DiffPriv Logo](https://docs.google.com/drawings/d/e/2PACX-1vQ8A92uJpy4g09GFYxayNQXOvtl0wmXXkYFiteDFSaXVcfdbcm835wc_IjjlKHlM94rjdsM7H1Szzjq/pub?w=600)


DiffPriv is a collection of different defferential privacy algorithms. From the Laplace Mechanism
to the simple Random Response mechanism, use differential privacy in your data easily with _DiffPriv_.

### Our world is full of data. Our world *is* data.
#### So let's protect it. Easily.

![photo of room with light up text saying, 'Data has a better idea'](https://images.unsplash.com/photo-1527474305487-b87b222841cc?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=500&q=60)

Photo by Franki Chamaki.


## How to Use

The first method we will use is the _Random Response Mechanism_. To use this, we use the `random` function.
You only need to pass *one* parameter. 

    random(response_list)

Response list, the parameter, is the list of data or responses in a form. 


