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        "description": "## Introduction\nThis project implements the connector method proposed in the paper [Supporting Confidentiality in Process Mining Using Abstraction and Encryption](https://www.researchgate.net/publication/338432872_Supporting_Confidentiality_in_Process_Mining_Using_Abstraction_and_Encryption) and [Ensuring Confidentiality in Process Mining](https://www.researchgate.net/publication/330042256_Ensuring_Confidentiality_in_Process_Mining).\n## Python package\nThe implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:\n\n```shell\npip install p-connector-dfg\n```\n\n## Usage\n\n```python\nfrom p_connector_dfg.privacyPreserving import privacyPreserving\n\nela_path = \".\\intermediate_results\\ela_connector.xml\"\nela_method = \"Connector Method\"\nela_desired_analyses = ['directly follows graph', 'process discovery']\n\nactivity_activity_matrix_path = r\".\\intermediate_results\\test.csv\"\n\ndfg_path = \"./DFG.svg\"\nfreq_threshold = 0.0\n\n#Connector structure parameters--------------\nrelation_depth = True #if you want to have relation depth in the connector structure\ntrace_length = True # if you want to have trace length in the connector structure\ntrace_id = True # if you want to have a fake trace id in the connector structure\n\nevent_log = \"sample_log.xes\"\nkey = 'DEFPASSWORD12345'\n\npp = privacyPreserving(event_log)\npp.apply_privacyPreserving(key, ela_path, ela_method, ela_desired_analyses, event_log, relation_depth = relation_depth, trace_length = trace_length, trace_id = trace_id)\n\npp.result_maker_ela(ela_path, True,True, True, freq_threshold, dfg_path, activity_activity_matrix_path = activity_activity_matrix_path,key = key)\n```\n\n\n",
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