Miguel Morales-Sandoval

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.

Secure data exchange across the distributed computing continuum is essential for decision-making processes. However, achieving this securely is challenging due to the heterogeneous nature of the continuum, where each infrastructure presents distinct requirements and capabilities in computing, storage, and energy. This paper presents Xook-Sec, a framework for building secure data-sharing systems within the computing continuum. Xook-Sec is grounded in three key concepts: Policy-as-Code (PaC), Infrastructure-as-Code (IaC), and architectural patterns. PaC enables organizations to integrate multiple security services with applications distributed across different infrastructures managed via IaC. Architectural patterns support the customization and optimization of security services. Xook-Sec enables organizations to define multiple data-sharing patterns, taking into account various security requirements and infrastructure characteristics. We implemented a Xook-Sec prototype and conducted case studies including sensitive medical and satellite imagery. Our evaluation shows that Xook-Sec is both flexible and efficient, outperforming comparable state-of-the-art solutions. Specifically, in direct comparisons with Jenkins and Nextflow, Xook-Sec achieves performance improvements of up to 50% and 40%, respectively.

It is crucial for organizations to ensure that their business processes are executed accurately and comply with internal policies and requirements. Process mining is a discipline of data science that exploits business process execution data to analyze and improve business processes. It provides a data-driven approach to understanding how processes actually work in practice. Conformance checking is one of the three most relevant process mining tasks. It consists of determining the degree of correspondence or deviation between the expected (or modeled) behavior of a process vs the real one observed and revealed from the historical events recorded in an event log during the execution of each instance of the process. Under a big data scenario, traditional conformance checking methods struggle to analyzing the instances or traces in large event logs, increasing the associated computational cost. In this article, we study and address the conformance-checking task supported by a traces selection approach that uses representative sample data of the event log and thus reduces the processing time and computational cost without losing confidence in the obtained conformance value. As main contributions, we present a novel conformance checking method that (i) takes into account the data dispersion that exists in the event log data using a statistic measure, (ii) determines the size of the representative sample of the event log for the conformance checking task, and (iii) establishes selection criteria of traces based on the dispersion level. The method was validated and evaluated using fitness, precision, generalization, and processing time metrics by experiments on three actual event logs in the health domain and two synthetic event logs. The experimental evaluation and results revealed the effectiveness of our method in coping with the problem of conformance between a process model and its corresponding large event log.

Process Mining as a Service (PMaaS) could enable organizations to delegate the analysis of their business processes to third parties in the cloud and then avoid costs related to storage and processing. However, this involves outsourcing event log data to the service provider, which can be honest but curious and learn from the data in clear. This potentially compromises the confidentiality and privacy of the event log data that can be sensitive, for example, in the healthcare domain. Thus, it is necessary to both safeguard the privacy of the event log data and guarantee their usefulness in the process mining tasks. This paper presents a study and experimental evaluation of two cryptographic-based methods suitable for PMaaS with security and privacy guaranties, tested under the process discovery task and evaluated through a set of experiments in terms of utility loss, performance and computational cost, using available event logs in the healthcare domain.