Getting Started

Welcome to the Getting Started guide for metaprivBIDS. This Python build tool enables a user to calculate a variety of different data privacy metrics on tabular data from a user interface.

installation

The metaprivBIDS software runs on multiple platforms (e.g. Linux, MacOS, Windows) that have a Python 3.7 installation. It is recommended (but not required) to first create a virtual environment. This can be done with venv or, if pygraphviz fails (as it happens), with conda.

python -m venv metapriv
source metapriv/bin/activate

or

conda config --add pkgs_dirs ~/conda_pkgs
conda create --name venv -c conda-forge "python>=3.7" graphviz pygraphviz r-base r-sdcMicro rpy2
conda activate venv

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

cd metaprivBIDS
pip install -e .

Basic Usage

Once installed, you can call and execute the program globally from any directory using the terminal/command prompt. This means you don’t need to navigate to the program’s installation folder; you can run it from anywhere.

metaprivBIDS

prompting the program to start.

Command-Line Execution

After following the installation guide, the metrics within the MetaprivBIDS tool can be called through an import statement without making use of the GUI.

e.g.

from metaprivBIDS.metaprivBIDS.corelogic.metapriv_corelogic import metaprivBIDS_core_logic
metapriv = metaprivBIDS_core_logic()

# Load the data
data_info = metapriv.load_data('metaprivBIDS/Use_Case_Data/adult_mini.csv')

# Inspect {column, unique value count, column type}
data = data_info["data"]
print("Column Types:",'\n')
print(data_info["column_types"],'\n')

# Select Quasi-Identifiers
selected_columns = ["age", "education", "marital-status", "occupation", "relationship","sex","salary-class"]
results_k_global = metapriv.find_lowest_unique_columns(data, selected_columns)
print('Find Influential Columns:','\n')
print(results_k_global)

# Compute Personal Information Factor
pif_value, cig_df = metapriv.compute_cig(data, selected_columns)
print("PIF Value:", pif_value)
print("CIG DataFrame:")
print(cig_df)


# Run SUDA2 computation
results_suda = metapriv.compute_suda2(data, selected_columns, sample_fraction=0.3, missing_value=-999)

# Access results
data_with_scores = results_suda["data_with_scores"]
attribute_contributions = results_suda["attribute_contributions"]
attribute_level_contributions = results_suda["attribute_level_contributions"]

Next Steps

Explore the Examples to see Interactive Tutorial of how to navigate the graphical user interface for MetaprivBIDS.