CONFINE: Preserving Data Secrecy in Decentralized Process Mining

TL;DR

CONFINE is a decentralized framework that enables secure inter-organizational process mining by preserving data confidentiality using Trusted Execution Environments, facilitating collaborative analysis without exposing sensitive information.

Contribution

The paper introduces CONFINE, a novel approach and toolset that allows process mining across multiple organizations while maintaining data privacy through secure trusted execution environments.

Findings

Secure process mining with confidentiality preserved

Decentralized architecture using Trusted Execution Environments

Effective collaboration without data exposure

Abstract

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.