pm-cedp-qdp 0.0.1

Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing

Introduction

This project implements the quantification of privacy leakage for differential privacy mechanisms in continuous event data publishing.

Python package

The implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:

pip install pm-cedp-qdp

Usage

from pm_cedp_qdp.qdp import QDP

if __name__ == '__main__':
    log_name = "BPI2012App.xes"
    state_window = 200 # a large number will consider the entire prefix/suffix of traces
    state_direction = "backward"  # backward (prefix) or forward (suffix)
    export_csv = log_name[:-4] + "_" + str(state_window) + "_" + state_direction + ".csv"
    recursive = True #This will continue quantifying releases until there is no incomplete trace. Otherwise, only one release is quantified.
    only_complete_traces = False #If you want to only consider the complete traces for generating temporal correlations.
    epsilon = 0.01
    qdp = QDP()
    FPL, BPL, TPL = qdp.apply(log_name,epsilon,export_csv,recursive=recursive,
                              only_complete_traces=only_complete_traces, state_window = state_window, state_direction = state_direction)