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! View the docs

Downloading DiffPriv

To download, open up your command prompt and type

    pip install DiffPriv # alternatively, pip install DiffPriv==v2.0.0a3, for the latest alpha

or from the source repo:

    git clone https://github.com/Quantalabs/DiffPriv
    # switch to `v2` branch for the alpha version
    python setup.py install

Conda Envioronment

Conda installation is avaliable through conda-forge:

    conda install -c conda-forge diffpriv
    conda install -c conda-forge/label/diffpriv_dev diffpriv # alpha release

View the docs - or view the docs to the latest alpha release - 2.0.0a3