Source code for pm4py.privacy

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from pm4py.objects.log.obj import EventLog
import pandas as pd
from typing import Union

[docs]def anonymize_differential_privacy(log: Union[EventLog, pd.DataFrame], epsilon: float = 1.0, k: int = 10, p: int = 20) -> pd.DataFrame: """ Protect event logs with differential privacy. Differential privacy is a guarantee that bounds the impact the data of one individual has on a query result. Control-flow information is anonymized with SaCoFa. This algorithm inserts noise into a trace-variant count, through the step-wise construction of a prefix tree. Contextual-information, like timestamps or resources, is anonymized with PRIPEL. This technique enriches a control-flow anonymized event log with contextual information from the original log, while still achieving differential privacy. PRIPEL anonymizes each event's timestamp and other attributes, that are stored as strings, integers, floats, or booleans. Please install diffprivlib (pip install diffprivlib==0.5.2) to run our algorithm. 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. 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. :param log: event log / Pandas dataframe :param epsilon: the strength of the differential privacy guarantee. The smaller the value of epsilon, the stronger the privacy guarantee that is provided. :param k: the maximal length of considered traces in the prefix tree. We recommend setting k, that roughly 80% of all traces from the original event log are covered. :param p: the pruning parameter, which denotes the minimum count a prefix has to have in order to not be discarded. The dependent exponential runtime of the algorithms is mitigated by the pruning parameter. :rtype: ``pd.DataFrame`` .. code-block:: python3 import pm4py event_log = pm4py.read_xes("running-example.xes") anonymized_event_log = pm4py.anonymize_differential_privacy(event_log, epsilon=1.0, k=10, p=20) """ from pm4py.algo.anonymization.trace_variant_query import algorithm as trace_variant_query sacofa_result = trace_variant_query.apply(log=log, variant=trace_variant_query.Variants.SACOFA, parameters={"epsilon": epsilon, "k": k, "p": p}) from pm4py.algo.anonymization.pripel import algorithm as pripel anonymized_log = pripel.apply(log, sacofa_result, epsilon=epsilon) return anonymized_log