Process mining techniques use event data to discover process models, to check the conformance of prede?ned process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history on the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quanti?ed. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis.

Replaying history on process models for conformance checking and performance analysis

https://doi.org/10.1016/j.jbi.2022.103994

Process mining for healthcare: Characteristics and challenges

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

https://www.mdpi.com/1660-4601/17/5/1612/pdf

Privacy-preserving process mining in healthcare

Event logs that originate from information systems enable comprehensive analysis of business processes, e.g., by process model discovery. However, logs potentially contain sensitive information about individual employees involved in process execution that are only partially hidden by an obfuscation of the event data. In this paper, we therefore address the risk of privacy-disclosure attacks on event logs with pseudonymized employee information. To this end, we introduce PRETSA, a novel algorithm for event log sanitization that provides privacy guarantees in terms of k-anonymity and t-closeness. It thereby avoids disclosure of employee identities, their membership in the event log, and their characterization based on sensitive attributes, such as performance information. Through step-wise transformations of a prefix-tree representation of an event log, we maintain its high utility for discovery of a performance-annotated process model. Experiments with real-world data demonstrate that sanitization with PRETSA yields event logs of higher utility compared to methods that exploit frequency-based filtering, while providing the same privacy guarantees.

PRETSA: Event Log Sanitization for Privacy-aware Process Discovery

Protection of data privacy from vulnerability using two-fish technique with Apriori algorithm in data mining

Process mining aims to provide insights into the actual processes based on event data. These data are widely available and often contain private information about individuals. On the one hand, knowing which individuals (known as resources) performed specific activities can be used for resource behavior analyses like role mining and is indispensable for bottleneck analysis. On the other hand, event data with resource information are highly sensitive. Process mining should reveal insights in the form of annotated models, but should not reveal sensitive information about individuals. In this paper, we show that the problem cannot be solved by naive approaches like encrypting data, and an anonymized person can still be identified based on a few well-chosen events. We, therefore, introduce a decomposition method and a collection of techniques that preserve the privacy of the individuals, yet, at the same time, roles can be discovered and used for further bottleneck analyses without revealing sensitive information about individuals. To evaluate our approach, we have implemented an interactive environment and applied our approach to several real-life and artificial event logs.

Mining Roles from Event Logs While Preserving Privacy

Process mining techniques help to improve processes using event data. Such data are widely available in information systems. However, they often contain highly sensitive information. For example, healthcare information systems record event data that can be utilized by process mining techniques to improve the treatment process, reduce patient’s waiting times, improve resource productivity, etc. However, the recorded event data include highly sensitive information related to treatment activities. Responsible process mining should provide insights about the underlying processes, yet, at the same time, it should not reveal sensitive information. In this paper, we discuss the challenges regarding directly applying existing well-known group-based privacy preservation techniques, e.g., k -anonymity, l -diversity, etc, to event data. We provide formal definitions of attack models and introduce an effective group-based privacy preservation technique for process mining. Our technique covers the main perspectives of process mining including control-flow, time, case, and organizational perspectives. The proposed technique provides interpretable and adjustable parameters to handle different privacy aspects. We employ real-life event data and evaluate both data utility and result utility to show the effectiveness of the privacy preservation technique. We also compare this approach with other group-based approaches for privacy-preserving event data publishing.

Group-based privacy preservation techniques for process mining

Process mining aims to provide insights into the actual processes based on event data These data are widely available and often contain private information about individuals Consider for example health-care information systems recording highly sensitive data related to diagnosis and treatment activities Process mining should reveal insights in the form of annotated models, yet, at the same time, should not reveal sensitive information about individuals In this paper, we discuss the challenges regarding directly applying existing well-known privacy-preserving techniques to event data We introduce the TLKC-privacy model for process mining that provides privacy guarantees in terms of group-based anonymization It extends and customizes the LKC-privacy model presented to deal with high-dimensional, sparse, and sequential trajectory data Experiments on real-life event data demonstrate that our privacy model maintains a high utility for process discovery and performance analyses while preserving the privacy of the cases

TLKC-Privacy Model for Process Mining.

Privacy and confidentiality are very important prerequisites for applying process mining to comply with regulations and keep company secrets. This article provides a foundation for future research ...

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy and confidentiality are very important prerequisites for applying process mining in order to comply with regulations and keep company secrets. This paper provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to an motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.

Majid Rafiei

Papers

Mining Roles from Event Logs While Preserving Privacy

Group-based privacy preservation techniques for process mining

TLKC-Privacy Model for Process Mining.

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Privacy and Confidentiality in Process Mining -- Threats and Research Challenges