# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 5 Conclusions and Future Work  
This paper introduced a novel goal-oriented model evaluation methodology for privacy-preserving process mining, specifically designed for diverse application domains. The methodology is implemented through operational scenarios to address different enquiries pertaining to these models. The objectives, activities, inputs, and outputs of each stage within the proposed methodology were examined, followed by a case study conducted to assess its feasibility in facilitating improvements to the PPPM model. A sample GQM table was constructed for the case study, PPPM activities were conducted, the pre-defined inquiries were addressed, and the outcomes were systematically evaluated. After applying the privacy-preserving model, discovered process models qualities and runtime performances were measured with corresponding metrics. Areas for potential enhancement were systematically identified. Table 10 presents a comprehensive summary of the SWOT analysis [39] conducted on the proposed methodology for applying PPPM across various domains. The SWOT analysis indicates that the strengths and opportunities associated with the proposed methodology outweigh its weaknesses and potential threats. Despite the proliferation of privacy-preserving approaches in process mining, such as event log anonymization and differential privacy, future research must challenge the tacit presumption that these techniques inherently comply with the requirements of theGeneral Data Protection Regulation (GDPR). Inherent in this assumption are several risks, including incomplete compliance, an illusory sense of legal protection, and the disregard of fundamental GDPR principles like the lawful basis for processing, data subject rights (such as access and erasure), and accountability obligations. Technical safeguards alone do not suffice to address challenges such as consent management, purpose limitation, and demonstrable compliance, as mandated by Articles 5, 6, and 30 of the GDPR. To address this gap, an alternative direction for future work may focus on the development of a structured GDPR-oriented evaluation framework for privacy-preserving process mining. A robust framework ought to reconcile legal and ethical imperatives with technical evaluation criteria, thereby enabling researchers and practitioners to systematically assess whether proposed approaches genuinely satisfy GDPR obligations. This strategy would not only bolster regulatory compliance but also underpin the development of process-mining systems that are inherently privacy-aware by design and resilient to evolving data protection standards. Furthermore, future research may concentrate on expanding the scope of the current case study to encompass multiple domains, thereby assessing the generalization and adaptability of the proposed methodology. Integrating a broader and more extensive collection of real-world event logs could provide a more rigorous assessment of the approach's robustness and scalability. Furthermore, the incorporation of automated privacy-preserving models, including adaptive differential privacy and context-aware anonymization, has the potential to improve both usability and regulatory compliance. Comparative analyses of alternative methodologies, coupled with longitudinal studies aimed at evaluating the long-term effects on process performance and decision-making, represent promising avenues for future research. Conceptualization, I.I., T.G.E. and A.K.-T.; methodology, I.I. ; validation, I.I. ; investigation, I.I. ; writing--original draft preparation, I.I. ; writing--review and editing, T.G.E. and A.K.-T.; supervision, A.K.-T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare that no external funding or support was received for this research. All contributions are solely those of the authors.