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Privacy Aware Role Mining in Process mining

Project description

Introduction

This project implements the decomposition method proposed in the paper Mining Roles From Event Logs While Preserving Privacy.

Python package

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

pip install pp-role-mining

Usage

from pp_role_mining.privacyPreserving import privacyPreserving

#for fixed_value technique
NoSubstitutions = 2
#for selective technique
MinMax = [True, True] #if you want to perturb both lower and upper bound
#for frequency_based technique
FixedValue = 0 #to combine the fixed_value techniue with the frequency_based technique (FixedValue=0 is only frequency_based without any fixed value added to the number of substitutions)

show_final_result = False

event_log = "running_example.xes"
# event_log = "pp_running_example.xes"
technique = 'fixed_value'  # fixed_value, selective, frequency_based
resource_aware = True #true if we want to consider resources while allocating activity substitutions. Otherwise it is False
hashedActivities = True #if you want to produce hash of activities
exportPrivacyAwareLog = True #if you want to export the log with the perturbed activities
privacy_aware_log_path = "pp_" + event_log
#
pp = privacyPreserving(event_log)
pp.apply_privacyPreserving(technique, resource_aware, exportPrivacyAwareLog, show_final_result, hashedActivities, NoSubstitutions=NoSubstitutions, MinMax=MinMax,
                           FixedValue=FixedValue, privacy_aware_log_path=privacy_aware_log_path, event_attribute2remove=["Activity", "Resource", "Costs"], case_attribute2remove=["creator"])

Project details


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