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.

中文翻译：

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PC4PM：过程挖掘中保护隐私/机密性的工具

流程挖掘使企业主能够使用信息系统中广泛可用的事件数据来发现和分析他们的实际流程。事件数据包含详细信息，这对于提供见解非常有价值。但是，此类详细数据通常包含高度机密和私密的信息。因此，过程挖掘中的隐私和机密性问题变得越来越重要，并且正在引入新技术。为了使这些技术易于访问，需要开发新工具来集成引入的技术并根据用户的需求将用户引导到适当的解决方案。在本文中，我们提出了一个基于 Python 的基础设施，在流程挖掘中实现和集成最先进的隐私/机密保护技术。我们的工具为隐私保护数据发布、风险分析和数据效用分析提供了一个易于使用的基于 Web 的用户界面。该工具还提供了一组可用于支持隐私/机密保护的匿名化操作。该工具管理标准 XES 事件日志和非标准事件数据。我们还存储和管理隐私元数据，以跟踪隐私/机密保护技术所做的更改。