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'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from enum import Enum
from typing import Optional, Dict, Any, Union
import pandas as pd
from pm4py.algo.anonymization.trace_variant_query.variants import laplace, sacofa # , sapa
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils
class Variants(Enum):
LAPLACE = laplace
SACOFA = sacofa
DEFAULT_VARIANT = Variants.SACOFA
def apply(log: Union[EventLog, pd.DataFrame], variant=DEFAULT_VARIANT,
parameters: Optional[Dict[Any, Any]] = None) -> EventLog:
"""
Applies a trace variant query to an event log. A trace variant query returns an event log that
captures trace variants and their frequencies in a differentially private manner, in other words it returns an
anonymized trace variant distribution. Such a step is essential, given that even the publication of activity
sequences from an event log, i.e., with all attribute values and timestamps removed, can be sufficient to link the
identity of individuals to infrequent activity sequences.
Variant Laplace is described in:
Mannhardt, F., Koschmider, A., Baracaldo, N. et al. Privacy-Preserving Process Mining. Bus Inf Syst Eng 61,
595–614 (2019). https://doi.org/10.1007/s12599-019-00613-3
Variant SaCoFa is described in:
S. A. Fahrenkog-Petersen, M. Kabierski, F. Rösel, H. van der Aa and M. Weidlich, "SaCoFa: Semantics-aware
Control-flow Anonymization for Process Mining," 2021 3rd International Conference on Process Mining (ICPM), 2021,
pp. 72-79, doi: 10.1109/ICPM53251.2021.9576857.
Variant DF-Laplace:
Parameters
-------------
log
Log
variant
Variant of the algorithm to apply, possible values:
-Variants.LAPLACE
-Variants.SACOFA
parameters
Parameters of the algorithm, including:
-Parameters.EPSILON -> Strength of the differential privacy guarantee
-Parameters.K -> Maximum prefix length of considered traces for the trace-variant-query
-Parameters.P -> Pruning parameter of the trace-variant-query. Of a noisy trace variant, at least P traces
must appear. Otherwise, the trace variant and its traces won't be part of the result of the
trace variant query.
Returns
--------------
anonymized_trace_variant_distribution
An anonymized trace variant distribution as an EventLog
"""
if parameters is None:
parameters = {}
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG)
tvq_log = exec_utils.get_variant(variant).apply(log, parameters=parameters)
if (len(tvq_log) == 0):
raise ValueError(
"The pruning parameter p is probably too high. The result of the trace variant query is empty. Of a noisy trace "
"variant, at least p traces must appear. Otherwise, the trace variant and its traces won't be part of the "
"result of the trace variant query.")
tvq_log = log_converter.apply(tvq_log, variant=log_converter.Variants.TO_DATA_FRAME)
return tvq_log
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