Metadata-Version: 2.1
Name: DiffPriv
Version: 1.0.0a0
Summary: A Differential Privacy Package
Home-page: https://github.com/Quantalabs/DiffPriv
Author: Quantalabs
License: UNKNOWN
Project-URL: Documentation, https://quantalabs.github.io/DiffPriv/docs/
Project-URL: Source, https://github.com/Quantalabs/DiffPriv
Project-URL: Tracker, https://github.com/Quantalabs/DiffPriv/issues
Project-URL: Changelog, https://quantalabs.github.io/DiffPriv/CHANGELOG
Project-URL: Download, https://pypi.org/project/DiffPriv/#files
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)

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> The truth is more important than everâ€”let's make sure easy privacy protection is available.

Differential privacy should be simple. Now that data defines our world, we need to look at the cost of privacy. Let's make protecting privacy easy.

<br><br>

## What is differential privacy?

Differential privacy allows for data to be preserved while making sure that attackers cannot gain access to an individual's data. Even if you publish summary statistics (like average age of participants, unlabeled addresses of participants, etc.), attackers can gain access to *individual* data (like age of *each* participant, *labeled* addresses of participants, etc.). In order to achieve this, differential privacy slightly changes the actual dataset to make sure that any uncovered data will not give away personal information. See below for how to get started!

<br>

## Downloading DiffPriv
To download, open up your command prompt and type
```sh
    pip install DiffPriv
```
or from the source repo:
```sh
    git clone https://github.com/Quantalabs/DiffPriv
    cd diffpriv
    python setup.py install
```
### Conda Envioronment
We currently do not have our package on Anaconda, however, we are working on getting it on conda, and should be available soon. However, there *is* a workaround for conda systems. Try building from the source with:

```sh
git clone https://github.com/Quantalabs/DiffPriv
cd DiffPriv
```
Then, create a `conda` virtual environment which should initialize `pip` with:
```sh
conda update conda
conda create -n DiffPriv python=3.9 anaconda
conda activate DiffPriv
```
Lastly, install dependencies with:
```sh
conda install numpy
pip install luddite  # luddite is not available on conda
```
Now, you can build the package from the source with:
```sh
conda setup.py install
```


