Process mining techniques such as process discovery and conformance checking provide insights into actual processes by analyzing event data that are widely available in information systems. These data are very valuable, but often contain sensitive information, and process analysts need to balance confidentiality and utility. Privacy issues in process mining are recently receiving more attention from researchers which should be complemented by a tool to integrate the solutions and make them available in the real world. In this paper, we introduce a Python-based infrastructure implementing state-of-the-art privacy preservation techniques in process mining. The infrastructure provides a hierarchy of usages from single techniques to the collection of techniques, integrated as web-based tools. Our infrastructure manages both standard and non-standard event data resulting from privacy preservation techniques. It also stores explicit privacy metadata to track the modifications applied to protect sensitive data.

中文翻译：

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过程挖掘中隐私保护数据发布的实践方面

流程挖掘技术（例如流程发现和一致性检查）通过分析在信息系统中广泛可用的事件数据来洞察实际流程。这些数据非常有价值，但往往包含敏感信息，流程分析师需要平衡机密性和实用性。过程挖掘中的隐私问题最近受到研究人员的更多关注，这应该辅之以集成解决方案并使它们在现实世界中可用的工具。在本文中，我们介绍了一个基于 Python 的基础设施，在流程挖掘中实现了最先进的隐私保护技术。基础设施提供了从单一技术到技术集合的使用层次结构，集成为基于 Web 的工具。我们的基础设施管理由隐私保护技术产生的标准和非标准事件数据。它还存储显式隐私元数据以跟踪用于保护敏感数据的修改。