Metadata-Version: 2.1
Name: DiffPriv
Version: 1.0.0rc0
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
Description: ![DiffPriv Logo](https://docs.google.com/drawings/d/e/2PACX-1vQ8A92uJpy4g09GFYxayNQXOvtl0wmXXkYFiteDFSaXVcfdbcm835wc_IjjlKHlM94rjdsM7H1Szzjq/pub?w=600)
        
        [![Build Status](https://www.travis-ci.com/Quantalabs/DiffPriv.svg?branch=master)](https://www.travis-ci.com/Quantalabs/DiffPriv)
        [![PyPI Version](https://shields.mitmproxy.org/pypi/v/DIffPriv.svg)](https://pypi.org/project/DiffPriv)
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        [![License](https://img.shields.io/badge/License-GNU%20GPL%20v3.0-green)](https://github.com/Quantalabs/DiffPriv/blob/master/LICENSE)
        [![Downloads](https://pepy.tech/badge/diffpriv)](https://pepy.tech/project/diffpriv)
        [![Downloads](https://pepy.tech/badge/diffpriv/month)](https://pepy.tech/project/diffpriv/month)
        [![Downloads](https://pepy.tech/badge/diffpriv/week)](https://pepy.tech/project/diffpriv/week) 
        [![Chat on GitHub Discussions](https://img.shields.io/badge/Chat-on%20Discussions-green)](https://github.com/Quantalabs/DiffPriv/discussions)
        
        
        > 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==1.0.0b0 # This is a pre-release, so you need the version number
        ```
        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
        ```
        
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
