Metadata-Version: 2.1
Name: DiffPriv
Version: 2.0.0a2
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
Project-URL: Code Coverage, https://app.codecov.io/gh/Quantalabs/DiffPriv
Project-URL: Forum, https://github.com/Quantalabs/DiffPriv/discussions
Project-URL: Conda Forge, https://anaconda.org/conda-forge/diffpriv
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
License-File: LICENSE

**This is an alpha release, meaning it might not be stable. We suggest you install the latest stable build.**

![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 ever—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==v2.0.0a2
```
or from the source repo:
```sh
    git clone https://github.com/Quantalabs/DiffPriv
    cd diffpriv
    git switch v2.0.0-alpha1
    python setup.py install
```
### Conda Envioronment

You can install it from conda through the command:
```sh
conda install -c conda-forge/label/diffpriv_dev diffpriv
```

### Docs

Once installed, check out the docs at https://quantalabs.github.io/DiffPriv/v2a2/


