See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/342977483

Practical Aspect of Privacy-Preserving Data Publishing in Process Mining
Preprint · July 2020

CITATIONS

READS

074

2 authors:
Majid Rafiei

Wil Van der Aalst

RWTH Aachen University

RWTH Aachen University

11 PUBLICATIONS 58 CITATIONS

1,252 PUBLICATIONS 70,138 CITATIONSSEE PROFILE

Some of the authors of this publication are also working on these related projects:

Responsible Process Mining (RPM) View project

Outlier Detection in process Mining View project

All content following this page was uploaded by Majid Rafiei on 16 July 2020.
The user has requested enhancement of the downloaded file.

SEE PROFILEPractical Aspect of Privacy-Preserving Data
Publishing in Process Mining*Majid Rafiei[0000−0001−7161−6927] and Wil M.P. van der
Aalst[0000−0002−0955−6940]
Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

Abstract. Process mining techniques such as process discovery and conformance checking provide insights into actual processes by analyzing
event data that are widely available in information systems. These dataare very valuable, but often contain sensitive information, and process
analysts need to balance confidentiality and utility. Privacy issues in process mining are recently receiving more attention from researchers whichshould be complemented by a tool to integrate the solutions and makethem available in the real world. In this paper, we introduce a Pythonbased infrastructure implementing state-of-the-art privacy preservation
techniques in process mining. The infrastructure provides a hierarchy ofusages from single techniques to the collection of techniques, integrated
as web-based tools. Our infrastructure manages both standard and nonstandard event data resulting from privacy preservation techniques. It
also stores explicit privacy metadata to track the modifications applied
to protect sensitive data.
Keywords: Responsible process mining · Privacy preservation · Processmining · Event data

1Introduction

Process mining provides fact-based insights into actual business processes using
event data, which are often stored in the form of event logs. The three basic
types of process mining are process discovery, conformance checking, and process enhancement [1]. An event log is a collection of events, and each event isdescribed by its attributes. The main attributes required for process mining arecase id, activity, timestamp, and resource. Some of the event attributes may referto individuals, e.g., in the health-care context, the case id attribute may referto the patients whose data are recorded, and the resource attribute may refer tothe employees performing activities for the patients, e.g., nurses or surgeons.
Privacy issues in process mining are highlighted when the individuals’ data
are included in the event logs. According to the regulations such as the European
General Data Protection Regulation (GDPR) [12], organizations are compelled
to take the privacy of individuals into account while analyzing their data. The*
Funded under the Excellence Strategy of the Federal Government and the Länder. We also
thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.

2

Majid Rafiei and Wil M.P. vand der Aalst

2

PPPM
Privacy-Preserving
Process Mining

PM
Process Mining

Process MiningPM

necessity of responsibly analyzing private data has recently resulted in more
attention for privacy issues in process mining [10,6,4,5]. In [2], the authors introduce a web-based tool, ELPaaS, implementing the privacy preservation techniques introduced in [4] and [5]. ELPaaS gets the required parameters from users
and provides results, as CSV files, in email addresses of the users.
Figure 1 shows the general approachof privacy in process mining including
two main activities: Privacy-Preserving
Event Log
Event Log
(EL)
Abstraction (ELA)
Privacy-Preserving
Data Publishing (PPDP) and PrivacyData Publishing
PPDPELA
EL
Preserving Process Mining (PPPM).
Privacy-Aware
PPDP aims to hide the identity andEvent Log (EL')
the sensitive data of record owners inEL'
event data to protect their privacy.
PPPM aims to extend traditional process mining algorithms to work with thenon-standard data resulting from some
PPDP techniques. Note that PPPM alR≈R'Process Mining
Process Mining
Process Mining
gorithms are tightly coupled with theResult (R')
Result (R')
Result (R)corresponding PPDP techniques.
In this paper, we introduce a tool Fig. 1: The general approach of privacy inwhich mainly focuses on PPDP and process mining.
offers state-of-the-art privacy preservation techniques including the connector
method for securely discovering processes [9,10], the decomposition method for
privacy-aware role mining [6], and T LKC-privacy model for process mining [8].
The privacy metadata proposed in [7] are also embedded in the offered privacy
preservation techniques. Moreover, privacy in the context of process mining ispresented through PM4Py-WS (PMTK) [3] with a web-based interface which isa particular example to show that the provided privacy preservation techniques
can be added to the existing process mining tools for supporting PPPM.
The remainder of the paper is organized as follows. In Section 2, we demonstrate the functionality and characteristics of the tool. Section 3 outlines thematurity and availability of the tool, and Section 4 concludes the paper.

Functionality and Characteristics

In this section, we demonstrate the main functionalities and characteristics of
our stand-alone web-based tool, PPDP-PM, which is written in Python using
Django framework1 . Our tool has four main modules: event data management,
privacy-aware role mining, connector method, and TLKC-privacy. The event datamanagement module has two tabs to upload and manage the event data that
could be standard XES event logs2 or non-standard event data, called Event
Log Abstraction (ELA) [7]. In this module, an event log can be set as the input
1
2

https://www.djangoproject.com/
http://www.xes-standard.org/

Practical Aspect of Privacy-Preserving Data Publishing in Process Mining

Event datamanagement module.

3

Link to the GitHubproject.

The privacy-awarerole mining module.
The connector
method.
The TLKC-privacy
method.

Outputs are temporarily
stored here.

Add an output to the event
data management module.

Fig. 2: The privacy-aware role mining page in PPDP-PM.

for the privacy preservation techniques. The privacy-aware role mining module(Figure 2) implements the decomposition method supporting three different techniques: fixed-value, selective, and frequency-based [6]. After applying a technique,the privacy-aware event log in the XES format is provided in the corresponding“Outputs” section. The generated event log preserves the data utility for miningroles from resources without exposing who performs what.
The connector method implements an encryption-based method for discovering directly follows graphs [9,10]. It breaks the traces down into the collectionof directly-follows relations which are securely stored in a data structure. Afterapplying the method, the privacy-aware event data are provided in the corresponding “Outputs” section as an XML file with the ELA format [7]. The
T LKC-privacy module implements the T LKC-privacy model for process mining [8] that provides group-based privacy guarantees assuming four types ofbackground knowledge: set, multiset, sequence, and relative. T refers to the accuracy of timestamps in the privacy-aware event log, L refers to the power ofbackground knowledge, K refers to the k in the k-anonymity definition [11], andC refers to the bound of confidence regarding the sensitive attribute values inan equivalence class. Applying this method results in a privacy-aware event log
in the XES format that preserves data utility for process discovery and performance analysis. We also provide the same privacy preservation techniques in thecontext of an open-source process mining tool. Figure 3 shows a snippet of thehome page of the privacy integration in PMTK where process mining algorithms
can directly be applied to the privacy-aware event data.
Each privacy preservation technique in the tool is implemented as a Django
application that enables the simultaneous running of different techniques on anevent log. This architecture makes the whole project easy to maintain, and newtechniques can simply be integrated as independent applications. The outputs
for the privacy preservation techniques are provided independently for each technique and can be downloaded or stored in the event data repository. PPDP-PM
is designed in a way that provides a cycle of privacy preservation techniques, i.e.,
the privacy-aware event data, added to the event data repository, can be set as
the input for the techniques again as long as they are in the form of standard
XES event logs. To keep the process analysts aware of the modifications applied

4

Majid Rafiei and Wil M.P. vand der Aalst

Process mining algorithms
that can be applied to theresults from the privacy
preservation techniques.

Privacy preservation
techniques integratedin PMTK.

Fig. 3: The home page of the privacy integration in PM4Py-WS (PMTK).

to the privacy-aware event logs, the privacy metadata [7] specify the order of
the applied privacy preservation techniques. Moreover, the tool follows a naming approach to uniquely identify the privacy-aware event data based on nameof the technique, the creation time, and name of the event log.

3

Availability and Maturity

As mentioned, PPDP-PM is a web-based application written in Python. The
source code, a screencast, and other information are available in a GitHub
repository: https://github.com/m4jidRafiei/PPDP-PM. The privacy preservation techniques, explained in Section 2, and the integration into PMTK arealso available as separate GitHub repositories.3 To facilitate the usage and integration of the privacy preservation techniques, they are also published as standard Python packages (https://pypi.org/): pp-role-mining, p-connector-dfg, ptlkc-privacy, and p-privacy-metadata. Our infrastructure provides a hierarchy
of usages such that users can use each technique independently, they can use
PPDP-PM which integrates a set of privacy preservation techniques as a standalone web-based application, and they can also use the provided techniques ina process mining tool where the privacy preservation techniques are integrated.
The scalability of the tool varies w.r.t. the privacy preservation technique andthe size of the input event log. Based on our experiments, our tool can handle
real-world event logs, e.g., the BPI challenge datasets4 . However, it can still be
improved for industry-scale usage. PPDP-PM and its integration in PMTK arealso provided as Docker containers which can simply be hosted by the users:
https://hub.docker.com/u/m4jid.

4Conclusion

Event data often include highly sensitive information that needs to be considered by process analysts w.r.t. the regulations. In this paper, we introduced aPython-based infrastructure for dealing with privacy issues in process mining.
A web-based application was introduced implementing privacy-preserving data
34

https://github.com/m4jidRafiei/
https://data.4tu.nl/repository/collection:event logs realPractical Aspect of Privacy-Preserving Data Publishing in Process Mining

5

publishing techniques in process mining. We also showed the privacy integrationin PMTK as an open-source web-based process mining tool. The infrastructure
was designed in such a way that other privacy preservation techniques can beintegrated. We plan to cover different perspectives of privacy and confidentiality issues in process mining, and novel techniques are supposed to be integrated
into the introduced framework. We also invite other researchers to integrate theirsolutions as independent applications in the provided framework.

References
1. van der Aalst, W.M.P.: Process Mining - Data Science in Action, Second Edition.
Springer (2016). https://doi.org/10.1007/978-3-662-49851-4
2. Bauer, M., Fahrenkrog-Petersen, S.A., Koschmider, A., Mannhardt, F., van derAa, H., Weidlich, M.: Elpaas: Event log privacy as a service. In: Proceedings ofthe Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM
2019 (2019)
3. Berti, A., van Zelst, S.J., van der Aalst, W.M.P.: Pm4py web services: Easy development, integration and deployment of process mining features in any application stack. In: Proceedings of the Dissertation Award, Doctoral Consortium, andDemonstration Track at BPM 2019 (2019)
4. Fahrenkrog-Petersen, S.A., van der Aa, H., Weidlich, M.: PRETSA: event log sanitization for privacy-aware process discovery. In: International Conference on Process Mining, ICPM 2019, Aachen, Germany (2019)
5. Mannhardt, F., Koschmider, A., Baracaldo, N., Weidlich, M., Michael, J.: Privacypreserving process mining - differential privacy for event logs. Business & Information Systems Engineering 61(5), 595–614 (2019)
6. Rafiei, M., van der Aalst, W.M.P.: Mining roles from event logs while preserving
privacy. In: Business Process Management Workshops - BPM 2019 InternationalWorkshops, Vienna, Austria. pp. 676–689 (2019)
7. Rafiei, M., van der Aalst, W.M.P.: Privacy-preserving data publishing in processmining. In: Business Process Management Forum - BPM Forum 2020, Sevilla,Spain, September 13-18, 2020, Proceedings (2020)
8. Rafiei, M., Wagner, M., van der Aalst, W.M.P.: TLKC-privacy model for processmining. In: 14th International Conference on Research Challenges in Information
Science, RCIS 2020 (2020)
9. Rafiei, M., von Waldthausen, L., van der Aalst, W.M.P.: Ensuring confidentiality
in process mining. In: Proceedings of the 8th International Symposium on Datadriven Process Discovery and Analysis (SIMPDA 2018), Seville, Spain (2018)
10. Rafiei, M., von Waldthausen, L., van der Aalst, W.M.P.: Supporting condentiality
in process mining using abstraction and encryption. In: Data-Driven Process Discovery and Analysis - 8th IFIP WG 2.6 International Symposium, SIMPDA 2018,
and 9th International Symposium, SIMPDA 2019, Revised Selected Papers (2019)11. Sweeney, L.: k-anonymity: A model for protecting privacy. International Journalof Uncertainty, Fuzziness and Knowledge-Based Systems 10(05), 557–570 (2002)
12. Voss, W.G.: European union data privacy law reform: General data protection
regulation, privacy shield, and the right to delisting. Business Lawyer 72(1) (2016)

View publication stats