论文标题
要量化流程挖掘的隐私
Towards Quantifying Privacy in Process Mining
论文作者
论文摘要
流程挖掘采用事件日志来提供对实际过程的见解。事件日志由信息系统记录，并包含有价值的信息帮助组织改善其流程。但是，这些数据还包括高度敏感的私人信息，这是应用过程挖掘时的主要问题。因此，过程挖掘中的隐私保护在重要性上正在增长，并正在引入新技术。需要评估拟议的隐私保护技术的有效性。衡量敏感的数据保护和数据实用程序保存非常重要。在本文中，我们提出了一种量化隐私保护技术有效性的方法。我们引入了两种量化披露风险以评估敏感数据保护方面的措施。此外，提出了一项措施来量化主要过程采矿活动的数据实用性保护。提出的措施已使用各种现实生活事件日志测试。
Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.
