'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from enum import Enum
from typing import Optional, Dict, Any, Union
import pandas as pd
from pm4py.algo.anonymization.pripel.util.AttributeAnonymizer import AttributeAnonymizer
from pm4py.algo.anonymization.pripel.util.TraceMatcher import TraceMatcher
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils
[docs]
class Parameters(Enum):
BLOCKLIST = "blocklist"
[docs]
def apply_pripel(log, tv_query_log, epsilon, blocklist):
if (len(tv_query_log) == 0):
raise ValueError(
"Pruning parameter k is too high. The result of the trace variant query is empty. At least k traces must appear "
"in a noisy variant count to be part of the result of the query.")
for trace in log:
delTraceAttributes = set()
for attribute in trace.attributes.keys():
if pd.isnull(trace.attributes[attribute]):
delTraceAttributes.add(attribute)
if blocklist is not None:
if attribute in blocklist:
delTraceAttributes.add(attribute)
for attribute in delTraceAttributes:
trace.attributes.pop(attribute)
for event in trace:
delAttributes = set()
for attribute in event.keys():
if blocklist is not None:
if attribute in blocklist:
delAttributes.add(attribute)
if pd.isnull(event[attribute]):
delAttributes.add(attribute)
if not isinstance(event[attribute], (float, str, int, bool)) and attribute != "time:timestamp":
delAttributes.add(attribute)
for attribute in delAttributes:
event._dict.pop(attribute)
traceMatcher = TraceMatcher(tv_query_log, log)
matchedLog = traceMatcher.matchQueryToLog()
distributionOfAttributes = traceMatcher.getAttributeDistribution()
occurredTimestamps, occurredTimestampDifferences = traceMatcher.getTimeStampData()
attributeAnonymizer = AttributeAnonymizer()
anonymizedLog = attributeAnonymizer.anonymize(matchedLog, distributionOfAttributes, epsilon,
occurredTimestampDifferences,
occurredTimestamps)
for i in range(len(anonymizedLog)):
anonymizedLog[i].attributes['concept:name'] = str(i)
anonymizedLog[i].attributes.pop('variant', None)
anonymizedLog = log_converter.apply(anonymizedLog, variant=log_converter.Variants.TO_DATA_FRAME)
return anonymizedLog
[docs]
def apply(log: Union[EventLog, pd.DataFrame], traceVariantQuery: Union[EventLog, pd.DataFrame], epsilon: float,
parameters: Optional[Dict[Any, Any]] = None) -> EventLog:
"""
PRIPEL (Privacy-preserving event log publishing with contextual information) is a framework to publish event logs
that fulfill differential privacy. PRIPEL ensures privacy on the level of individual cases instead of the complete
log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the
application of a rich set of process analysis techniques.
PRIPEL is described in:
Fahrenkrog-Petersen, S.A., van der Aa, H., Weidlich, M. (2020). PRIPEL: Privacy-Preserving Event Log Publishing
Including Contextual Information. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process
Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham.
https://doi.org/10.1007/978-3-030-58666-9_7
Parameters
-------------
log
Event log
traceVariantQuery
An anonymized trace variant distribution as an EventLog
epsilon
Strength of the differential privacy guarantee
parameters
Parameters of the algorithm, including:
-Parameters.BLOCKLIST -> Some event logs contain attributes that are equivalent to a case id. For privacy
reasons, such attributes must be deleted from the anonymized log. We handle such attributes with this list.
Returns
------------
anonymised_log
Anonymised event log
"""
if parameters is None:
parameters = {}
blocklist = exec_utils.get_param_value(Parameters.BLOCKLIST, parameters, None)
return apply_pripel(log, traceVariantQuery, epsilon, blocklist)
