论文标题
在连续事件数据发布中量化时间隐私泄漏
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing
论文作者
论文摘要
流程挖掘采用从不同类型的信息系统中提取的事件数据来发现和分析实际过程。事件数据通常包含有关开展活动或进行活动的人的高度敏感信息。因此，流程挖掘的隐私问题正在受到越来越多的关注。为了减轻与隐私相关的风险，已经提出了几种隐私保护技术。差异隐私是提供强大隐私保证的这些技术之一。但是，提出的技术假定事件数据仅以一拍释放，而业务流程则不断执行。因此，事件数据反复发布，导致额外的风险。在本文中，我们证明了连续发布的事件数据不是独立的，并且当将相同的差异隐私机制应用于每个版本时，不同发行版之间的相关性可能导致隐私降解。我们以时间隐私泄漏的形式量化了这种隐私退化。我们将连续事件数据发布方案应用于现实生活事件日志，以演示隐私泄漏。
Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.
