Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals’ privacy. However, existing techniques for such pre-processing are limited to a process’ control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and 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. We demonstrate the feasibility of our framework in a case study with a real-world event log.

PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information

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

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.

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

Traditionally, research in Business Process Management has put a strong focus on centralized and intra-organizational processes. However, today's business processes are increasingly distributed, deviating from a centralized layout, and therefore calling for novel methodologies of detecting and responding to unforeseen events, such as errors occurring during process runtime. In this article, we demonstrate how to employ event-based failure prediction in business processes. This approach allows to make use of the best of both traditional Business Process Management Systems and event-based systems. Our approach employs machine learning techniques and considers various types of events. We evaluate our solution using two business process data sets, including one from a real-world event log, and show that we are able to detect errors and predict failures with high accuracy.

Event-based Failure Prediction in Distributed Business Processes

Process mining is a family of techniques for analyzing business processes based on event logs extracted from information systems Mainstream process mining tools are designed for intra-organizational settings, insofar as they assume that an event log is available for processing as a whole The use of such tools for inter-organizational process analysis is hampered by the fact that such processes involve independent parties who are unwilling to, or sometimes legally prevented from, sharing detailed event logs with each other In this setting, this paper proposes an approach for constructing and querying a common artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other The proposal leverages an existing platform for secure multi-party computation, namely Sharemind Since a direct implementation of DFG construction in Sharemind suffers from scalability issues, we propose to rely on vectorization of event logs and to employ a divide-and-conquer scheme for parallel processing of sub-logs The paper reports on experiments that evaluate the scalability of the approach on real-life logs

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Secure Multi-Party Computation for Inter-Organizational Process Mining

/pdf/shareprom-a-tool-for-privacy-preserving-inter-organizational-206l8fxz5z.pdf

Shareprom: A Tool for Privacy-Preserving Inter-Organizational Process Mining.

Process mining is a family of techniques for analysing business processes based on event logs extracted from information systems. Mainstream process mining tools are designed for intra-organizational settings, insofar as they assume that an event log is available for processing as a whole. The use of such tools for inter-organizational process analysis is hampered by the fact that such processes involve independent parties who are unwilling to, or sometimes legally prevented from, sharing detailed event logs with each other. In this setting, this paper proposes an approach for constructing and querying a common type of artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other. The proposal leverages an existing platform for secure multi-party computation, namely Sharemind. Since a direct implementation of DFG construction in Sharemind suffers from scalability issues, the paper proposes to rely on vectorization of event logs and to employ a divide-and-conquer scheme for parallel processing of sub-logs. The paper reports on an experimental evaluation that tests the scalability of the approach on real-life logs.

Gamal Elkoumy

Papers

Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Secure Multi-Party Computation for Inter-Organizational Process Mining

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

Shareprom: A Tool for Privacy-Preserving Inter-Organizational Process Mining.

Secure Multi-Party Computation for Inter-Organizational Process Mining