Privacy-Preserving Data Publishing in Process Mining

Rafiei, Majid; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-58638-6_8

Cited by 23 publications

(29 citation statements)

References 21 publications

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“…Annotations provide a structured way to make such data quality information accessible to process mining algorithms. Annotations have, for instance, been proposed to record cost [54] and for privacy-preserving transformations [39]. However, annotations are absent for event log quality.…”

Section: Related Workmentioning

confidence: 99%

Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations

Goel

Leemans

Martin

et al. 2022

ACM Trans. Knowl. Discov. Data

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Real-life event logs, reflecting the actual executions of complex business processes, are faced with numerous data quality issues. Extensive data sanity checks and pre-processing are usually needed before historical data can be used as input to obtain reliable data-driven insights. However, most of the existing algorithms in process mining, a field focusing on data-driven process analysis, do not take any data quality issues or the potential effects of data pre-processing into account explicitly. This can result in erroneous process mining results, leading to inaccurate or misleading conclusions about the process under investigation. To address this gap, we propose data quality annotations for event logs, which can be used by process mining algorithms to generate quality-informed insights. Using a design science approach, requirements are formulated, which are leveraged to propose data quality annotations. Moreover, we present the ‘Quality-Informed visual Miner’ plug-in to demonstrate the potential utility and impact of data quality annotations. Our experimental results, utilising both synthetic and real-life event logs, show how the use of data quality annotations by process mining techniques can assist in increasing the reliability of performance analysis results.

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Section: Related Workmentioning

confidence: 99%

Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations

Goel

Leemans

Martin

et al. 2022

ACM Trans. Knowl. Discov. Data

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“…In [13], the authors analyze data privacy and utility requirements for healthcare event data, and the suitability of privacy-preserving techniques is assessed. In [16], privacy metadata in process mining are discussed and a privacy extension for the XES standard (https://xes-standard.org/) is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…4.2 for quantifying data utility preservation after applying a privacy preservation technique. We use PPDP-PM [15] as a privacy preservation tool for process mining to apply the TLKC-privacy model [17] to a given event log. The TLKC-privacy model is a group-based privacy preservation technique which provides a good level of flexibility through various parameters such as the type and size (power) of background knowledge.…”

Section: Utility Loss Analysismentioning

confidence: 99%

“…PPDP aims to hide the identity and the sensitive data of record owners in event logs to protect their privacy. PPPM aims to extend traditional process mining algorithms to work with the non-standard event data so-called Event Log Abstraction (ELA) [16] that might result from PPDP techniques. Abstractions are intermediate results, e.g., a directly follows graph could be an intermediate result of a process discovery algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Towards Quantifying Privacy in Process Mining

Rafiei

Aalst

2021

Lecture Notes in Business Information Processing

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Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.

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“…In this regard, the Privacy-Preserving Process Mining (PPPM): The proposed Privacy-Preserving Process Mining (PPPM) framework seeks further privacy-preservation by adding noise to pseudonymized healthcare dataset [43]. For PPPM, the metadata with a detailed description of added noise type, quantity, and purpose must be shared with a Trusted Third Party (TTP) [41,43,[52][53][54].…”

Section: Ethical Issuesmentioning

confidence: 99%

Multilevel Privacy Assurance Evaluation of Healthcare Metadata

2021

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Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.

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Privacy-Preserving Data Publishing in Process Mining

Cited by 23 publications

References 21 publications

Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations

Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations

Towards Quantifying Privacy in Process Mining

Multilevel Privacy Assurance Evaluation of Healthcare Metadata

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