A TEE-based approach for preserving data secrecy in process mining with decentralized sources

2026-02-04

Journal of Information Security and Applications

AI Summary

This work addresses the challenge of confidentiality breaches in cross-organizational process mining, which often arise from sharing sensitive event logs and hinder collaborative analysis. To overcome this, the authors propose CONFINE, a novel approach that integrates Trusted Execution Environments (TEEs) into this domain for the first time. CONFINE employs a four-phase secure aggregation protocol that enables tamper-proof cross-organizational process mining without exposing raw event logs. To mitigate TEE memory constraints, the method introduces an innovative log segmentation mechanism. Experimental evaluation demonstrates that the solution incurs memory overhead that grows logarithmically with log size and linearly with the number of participating organizations. Results on both real-world and synthetic datasets confirm that CONFINE achieves strong security guarantees while maintaining scalability and practical deployability.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Problem

• process mining
• data secrecy
• inter-organizational
• confidentiality
• event logs

Innovation

• Trusted Execution Environment (TEE)
• Inter-organizational Process Mining
• Data Secrecy
• Event Log Segmentation
• Secure Multi-party Computation