DiffPriv 2.0.0a2

A Differential Privacy Package

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

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.

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!

Downloading DiffPriv

To download, open up your command prompt and type

    pip install DiffPriv==v2.0.0a2

or from the source repo:

    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:

conda install -c conda-forge/label/diffpriv_dev diffpriv

Docs

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