Metadata-Version: 2.1
Name: pm_cedp_qdp
Version: 0.0.1
Summary: Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing
Home-page: https://github.com/m4jidRafiei/QDP_CEDP
Author: Majid Rafiei
Author-email: majid.rafiei@pads.rwth-aachen.de
License: UNKNOWN
Project-URL: Source, https://github.com/m4jidRafiei/QDP_CEDP
Description: ## 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:
        
        ```shell
        pip install pm-cedp-qdp
        ```
        
        ## Usage
        ```python
        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)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
