Process mining aims to provide insights into the actual processes based on event data. These data are often recorded by information systems and are widely available. However, they often contain sensitive private information that should be analyzed responsibly. Therefore, privacy issues in process mining are recently receiving more attention. Privacy preservation techniques obviously need to modify the original data, yet, at the same time, they are supposed to preserve the data utility. Privacy-preserving transformations of the data may lead to incorrect or misleading analysis results. Hence, new infrastructures need to be designed for publishing the privacy-aware event data whose aim is to provide metadata regarding the privacy-related transformations on event data without revealing details of privacy preservation techniques or the protected information. In this paper, we provide formal definitions for the main anonymization operations, used by privacy models in process mining. These are used to create an infrastructure for recording the privacy metadata. We advocate the proposed privacy metadata in practice by designing a privacy extension for the XES standard and a general data structure for event data which are not in the form of standard event logs.

中文翻译：

---

流程挖掘中的隐私保护数据发布

流程挖掘旨在基于事件数据提供对实际流程的见解。这些数据通常由信息系统记录，并且广泛可用。但是，它们通常包含敏感的私人信息，应负责任地进行分析。因此，最近在过程挖掘中的隐私问题受到了越来越多的关注。隐私保护技术显然需要修改原始数据，但与此同时，应该保留数据实用程序。数据的隐私保护转换可能导致错误或误导性的分析结果。因此，需要设计新的基础结构来发布具有隐私意识的事件数据，其目的是提供有关事件数据上与隐私相关的转换的元数据，而无需透露隐私保护技术或受保护信息的细节。在本文中，我们提供了主要匿名操作的正式定义，供隐私模型在流程挖掘中使用。这些用于创建记录隐私元数据的基础结构。在实践中，我们通过设计XES标准的隐私扩展和非标准事件日志形式的事件数据的通用数据结构来倡导建议的隐私元数据。这些用于创建记录隐私元数据的基础结构。在实践中，我们通过设计XES标准的隐私扩展和非标准事件日志形式的事件数据的通用数据结构来倡导建议的隐私元数据。这些用于创建记录隐私元数据的基础结构。在实践中，我们通过设计XES标准的隐私扩展和非标准事件日志形式的事件数据的通用数据结构来倡导建议的隐私元数据。