pm4py.algo.anonymization.pripel.variants package#

PM4Py – A Process Mining Library for Python

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

Submodules#

pm4py.algo.anonymization.pripel.variants.pripel module#

PM4Py – A Process Mining Library for Python

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

class pm4py.algo.anonymization.pripel.variants.pripel.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

BLOCKLIST = 'blocklist'#

pm4py.algo.anonymization.pripel.variants.pripel.apply_pripel(log, tv_query_log, epsilon, blocklist)[source]#

pm4py.algo.anonymization.pripel.variants.pripel.apply(log: EventLog | DataFrame, traceVariantQuery: EventLog | DataFrame, epsilon: float, parameters: Dict[Any, Any] | None = None) → EventLog[source]#

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