In the contemporary business landscape, collaboration across multiple organizations offers a multitude of opportunities, including reduced operational costs, enhanced performance, and accelerated technological advancement. The application of process mining techniques in an inter-organizational setting, exploiting the recorded process event data, enables the coordination of joint effort and allows for a deeper understanding of the business. Nevertheless, considerable concerns pertaining to data confidentiality emerge, as organizations frequently demonstrate a reluctance to expose sensitive data demanded for process mining, due to concerns related to privacy and security risks. The presence of conflicting interests among the parties involved can impede the practice of open data sharing. To address these challenges, we propose our approach and toolset named CONFINE, which we developed with the intent of enabling process mining on process event data from multiple providers while preserving the confidentiality and integrity of the original records. To ensure that the presented interaction protocol steps are secure and that the processed information is hidden from both involved and external actors, our approach is based on a decentralized architecture and consists of trusted applications running in Trusted Execution Environments (TEE). In this demo paper, we provide an overview of the core components and functionalities as well as the specific details of its application.

翻译：在当今商业环境中，跨组织协作提供了诸多机遇，包括降低运营成本、提升绩效和加速技术进步。在跨组织场景中应用流程挖掘技术，利用记录的过程事件数据，能够协调多方合作努力，并促进对业务的深入理解。然而，由于涉及隐私和安全风险，组织通常不愿公开流程挖掘所需的敏感数据，这引发了关于数据机密性的重大关切。参与方之间的利益冲突可能阻碍开放数据共享的实践。为应对这些挑战，我们提出了名为CONFINE的方法与工具集，其开发目的是在保护原始记录机密性和完整性的前提下，实现对多来源过程事件数据的流程挖掘。为确保所提出的交互协议步骤的安全性，并使处理信息对参与方及外部行为者均不可见，我们的方法基于去中心化架构，由在可信执行环境（TEE）中运行的可信应用程序构成。在本演示论文中，我们将概述其核心组件、功能特性以及具体应用细节。