This Python build tool enables a given user to calculate a variety of different data privacy metrics on tabular data from a user interface.

• K-anonymity ¹
• ℓ-diversity ²
• Sample Unique Detection Algorithm (SUDA) ³
• Privacy Information Factor (PIF) ⁴

• Noise addition
• Field generalisation
• Rounded Approximation

Input can be in either CSV or TSV format. For meta information an option of load of json file is possible.

The metaprivBIDS software runs on multiple platforms (e.g. Linux, MacOS, Windows) that have a Python 3 installation. It is recommended (but not required) to first create a virtual environment.

python -m venv venv
source venv/bin/activate

You can then install metaprivBIDS by cloning the git respository.

git clone https://github.com/CPernet/metaprivBIDS.git

In the event of permission issues for system dependent files, you might want to set the pkgs_dirs option in Conda's configuration to use a directory that is writable by you.

conda config --add pkgs_dirs ~/conda_pkgs

Creates the enviroment.

conda create --name venv python=3.x.x

Activates the environment.

conda activate venv 

Graphviz requires system level dependencies and might need to be installed with

conda install graphviz pygraphviz

if not avaliable.

You can then install metaprivBIDS by cloning the git respository.

git clone https://github.com/CPernet/metaprivBIDS.git

To execute the program make sure all dependencies from pyproject.toml is avalible in a python 3.7 enviroment. This can be done by running

pip install -e . 

To execute the program run from command line

metaprivBIDS

prompting the program to start.

• https://github.com/JiscDACT/suda/blob/main/test/test_suda.py