差分隐私归纳式挖掘器
Differentially Private Inductive Miner
AI总结：
本文提出差分隐私归纳式挖掘器（DPIM），在保护事件轨迹个人数据的同时学习底层过程树，实验证明其在14个真实数据集上能以极小的效用损失生成忠实过程树。
摘要：
保护个人数据（例如过程挖掘中的事件轨迹）本质上是一项困难的任务，因为事件轨迹会泄露个人在过程模型中所触发路径的信息。然而，先前的事件轨迹匿名化方法，如k-匿名性或事件日志净化，在抵御此类泄露方面存在困难，尤其是在面对具有足够背景知识的对手时。在这项工作中，我们提供了一种方法，通过以隐私保护的方式学习底层过程树来应对敏感事件轨迹摘要化的挑战。我们通过所谓的差分隐私（DP）性质证明，从生成的摘要中无法对事件轨迹中的任何个人数据得出有用的推断。在技术方面，我们引入了归纳式挖掘器（Inductive Miner）的差分隐私近似版本（DPIM）。在实验上，我们通过评估众所周知的指标：适应度、精确度、简单性和泛化性，将我们的DPIM与归纳式挖掘器在14个真实世界事件轨迹上进行了比较。实验表明，我们的DPIM不仅保护了个人数据，而且生成了忠实的过程树，其效用损失相对于归纳式挖掘器而言很小。
英文摘要：
Protecting personal data about individuals, such as event traces in process mining, is an inherently difficult task since an event trace leaks information about the path in a process model that an individual has triggered. Yet, prior anonymization methods of event traces like k-anonymity or event log sanitization struggled to protect against such leakage, in particular against adversaries with sufficient background knowledge. In this work, we provide a method that tackles the challenge of summarizing sensitive event traces by learning the underlying process tree in a privacy-preserving manner. We prove via the so-called Differential Privacy (DP) property that from the resulting summaries no useful inference can be drawn about any personal data in an event trace. On the technical side, we introduce a differentially private approximation (DPIM) of the Inductive Miner. Experimentally, we compare our DPIM with the Inductive Miner on 14 real-world event traces by evaluating well-known metrics: fitness, precision, simplicity, and generalization. The experiments show that our DPIM not only protects personal data but also generates faithful process trees that exhibit little utility loss above the Inductive Miner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
