Privacy-Preserving Process Model Discovery Using Fully Homomorphic and Quantum-Safe Encryption

Process mining is a data-driven technique that acts as a bridge between data science and process management. One of its main tasks is enabling the identification of process models from event logs. However, when event logs contain sensitive data or compliance with data protection regulations is required, privacy concerns arise. To address these issues, we propose a novel process discovery method that ensures data confidentiality under an honest-but-curious scenario. Our method is the first to leverage Ring Learning With Errors (RLWE)-based encryption for process discovery, enabling computations over quantum-resistant encrypted data. Unlike existing cryptographic approaches for Privacy-Preserving Process Discovery (PPPD), the proposed method uses fully homomorphic and RLWE-based encryption to execute the required computations over ciphertexts while preserving the quality of the discovered process models. This leads to a significant reduction in execution time compared to other PPPD approaches. We evaluated our method using real-life event logs commonly employed to evaluate process discovery algorithms. The results demonstrate improved performance and enhanced privacy compared to prior approaches. This contribution represents a substantial advancement in PPPD, laying the foundation for a secure and feasible cryptographic framework.