Towards Quantifying Privacy in Process Mining

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

doi:10.1007/978-3-030-72693-5_29

Cited by 22 publications

(30 citation statements)

References 18 publications

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“…The result utility analysis evaluates the similarity of the specific results obtained from the privacy-aware event log with the same type of results obtained from the original event log, while the data utility analysis compares the privacy-aware event log with the original event log. As discussed in [29], the result utility analysis is highly dependent on the underlying algorithm generating specific results, and the data utility analysis provides a more general evaluation. We perform the evaluation for the three main perspectives including control-flow, organizational, and time perspectives.…”

Section: Methodsmentioning

confidence: 99%

“…Suppose that an adversary knows that a victim patient's data are in the event log (as a case), with little information about some event attributes that belongs to the patient, the adversary is able to connect the patient to the corresponding case id, so-called case disclosure [29]. Consequently, two types of sensitive personspecific information are revealed: (1) the complete sequence of events belonging to the case, and (2) sensitive case attributes.…”

Section: Motivation and Challengesmentioning

confidence: 99%

“…( 1) and ( 2) are generally called attribute disclosure. (1) is also called trace disclosure that is a specific type of attribute disclosure [29]. For example, if the adversary knows that two blood tests were performed for the victim patient, the only matching case is the case with id 2.…”

Section: Motivation and Challengesmentioning

confidence: 99%

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Group-Based Privacy Preservation Techniques for Process Mining

Rafiei¹,

Aalst²

2021

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Motivation and Challengesmentioning

confidence: 99%

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Group-Based Privacy Preservation Techniques for Process Mining

Rafiei¹,

Aalst²

2021

Preprint

Self Cite

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“…Pika et al [36] studied the impact of anonymization on process mining in healthcare without providing a concrete mechanism. In this line, Rafiei et al [40] provide privacy quantifications for both the disclosure risk and the utility loss and Nuñez von Voigt et al [52] quantify the re-identification risk resulted from the disclosure of event logs based on individual uniqueness. Both do not provide a solution.…”

Section: Current Workmentioning

confidence: 99%

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Elkoumy

Fahrenkrog-Petersen

Sani

et al. 2021

ACM Trans. Manage. Inf. Syst.

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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 on privacy-preserving and confidential process mining techniques. Main threats are identified and related to a 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.

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“…The PRIPEL application presents the PRIPEL method [2] which applies the notion of differential privacy to provide privacy guarantees for event logs. The privacy analysis application includes three components for analyzing disclosure risks, data utility [11], and FCB-anonymity [12].…”

Section: Functionality and Characteristicsmentioning

confidence: 99%

PC4PM: A Tool for Privacy/Confidentiality Preservation in Process Mining

Rafiei,

Schnitzler,

van der Aalst

2021

Preprint

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Process mining enables business owners to discover and analyze their actual processes using event data that are widely available in information systems. Event data contain detailed information which is incredibly valuable for providing insights. However, such detailed data often include highly confidential and private information. Thus, concerns of privacy and confidentiality in process mining are becoming increasingly relevant and new techniques are being introduced. To make the techniques easily accessible, new tools need to be developed to integrate the introduced techniques and direct users to appropriate solutions based on their needs. In this paper, we present a Python-based infrastructure implementing and integrating state-of-the-art privacy/confidentiality preservation techniques in process mining. Our tool provides an easy-to-use web-based user interface for privacy-preserving data publishing, risk analysis, and data utility analysis. The tool also provides a set of anonymization operations that can be utilized to support privacy/confidentiality preservation. The tool manages both standard XES event logs and non-standard event data. We also store and manage privacy metadata to track the changes made by privacy/confidentiality preservation techniques.

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Towards Quantifying Privacy in Process Mining

Cited by 22 publications

References 18 publications

Group-Based Privacy Preservation Techniques for Process Mining

Group-Based Privacy Preservation Techniques for Process Mining

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

PC4PM: A Tool for Privacy/Confidentiality Preservation in Process Mining

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